nm applet - Page 42 - Developer IT

What is hogging my connection?

- by SF.

At times it seems like dozens, if not hundreds of root-owned HTTP connections spring up. This is not much of a problem on LAN or WLAN as each of them seems to transfer very little, but if I use GPRS link, my ping times go into minutes (seriously, 80000ms is not infrequent!) and all connections grind to a halt waiting till these end. This usually lasts some 15 minutes and ends about when I start troubleshooting it for real. I've managed to capture a fragment of Nethogs output NetHogs version 0.8.0 PID USER PROGRAM DEV SENT RECEIVED ? root 37.209.147.180:59854-141.101.114.59:80 0.013 0.000 KB/sec ? root 37.209.147.180:59853-141.101.114.59:80 0.000 0.000 KB/sec ? root 37.209.147.180:52804-173.194.70.95:80 0.000 0.000 KB/sec 1954 bw /home/bw/.dropbox-dist/dropbox ppp0 0.000 0.000 KB/sec ? root 37.209.147.180:59851-141.101.114.59:80 0.000 0.000 KB/sec ? root 37.209.147.180:59850-141.101.114.59:80 0.000 0.000 KB/sec ? root 37.209.147.180:52801-173.194.70.95:80 0.000 0.000 KB/sec 13301 bw /usr/lib/firefox/firefox ppp0 0.000 0.000 KB/sec ? root unknown TCP 0.000 0.000 KB/sec Unfortunately, it doesn't display the owning process of these. Does anyone recognize these addresses or is able to suggest how to troubleshoot it further or disable it? Is it some automatic update or something like that? EDIT: per request; netstat -n, for obvious reason that normal netstat won't ever launch as all DNS requests are hogged just the same. netstat -n Active Internet connections (w/o servers) Proto Recv-Q Send-Q Local Address Foreign Address State tcp 0 1 93.154.166.62:51314 198.252.206.16:80 FIN_WAIT1 tcp 0 1 37.209.147.180:44098 198.252.206.16:80 FIN_WAIT1 tcp 0 1 37.209.147.180:59855 141.101.114.59:80 FIN_WAIT1 tcp 1 0 192.168.43.224:38237 213.189.45.39:443 CLOSE_WAIT tcp 1 0 93.154.146.186:35167 75.101.152.29:80 CLOSE_WAIT tcp 1 0 192.168.43.224:32939 199.15.160.100:80 CLOSE_WAIT tcp 1 0 192.168.43.224:55619 63.245.217.207:443 CLOSE_WAIT tcp 1 0 93.154.146.186:60210 75.101.152.29:443 CLOSE_WAIT tcp 1 0 192.168.43.224:32944 199.15.160.100:80 CLOSE_WAIT tcp 0 1 37.209.147.180:52804 173.194.70.95:80 FIN_WAIT1 tcp 1 0 93.154.146.186:46606 23.21.151.181:80 CLOSE_WAIT tcp 1 0 93.154.146.186:52619 107.22.246.76:80 CLOSE_WAIT tcp 415 0 93.154.146.186:36156 82.112.106.104:80 CLOSE_WAIT tcp 1 0 93.154.146.186:50352 107.22.246.76:443 CLOSE_WAIT tcp 1 0 192.168.43.224:55000 213.189.45.44:443 CLOSE_WAIT tcp 0 1 37.209.147.180:59853 141.101.114.59:80 FIN_WAIT1 tcp 1 0 192.168.43.224:32937 199.15.160.100:80 CLOSE_WAIT tcp 1 0 192.168.43.224:56055 93.184.221.40:80 CLOSE_WAIT tcp 415 0 93.154.146.186:36155 82.112.106.104:80 CLOSE_WAIT tcp 0 1 37.209.147.180:44097 198.252.206.16:80 FIN_WAIT1 tcp 1 0 93.154.146.186:35166 75.101.152.29:80 CLOSE_WAIT tcp 1 0 192.168.43.224:32943 199.15.160.100:80 CLOSE_WAIT tcp 1 0 93.154.146.186:46607 23.21.151.181:80 CLOSE_WAIT tcp 1 0 93.154.146.186:36422 23.21.151.181:443 CLOSE_WAIT tcp 1 0 192.168.43.224:36081 93.184.220.148:80 CLOSE_WAIT tcp 1 0 192.168.43.224:44462 213.189.45.29:443 CLOSE_WAIT tcp 1 0 192.168.43.224:32938 199.15.160.100:80 CLOSE_WAIT tcp 1 0 93.154.146.186:36419 23.21.151.181:443 CLOSE_WAIT tcp 0 497 93.154.166.62:51313 198.252.206.16:80 FIN_WAIT1 tcp 0 1 37.209.147.180:59851 141.101.114.59:80 FIN_WAIT1 tcp 0 1 37.209.147.180:44095 198.252.206.16:80 FIN_WAIT1 tcp 1 0 93.154.146.186:46611 23.21.151.181:80 CLOSE_WAIT tcp 1 0 192.168.43.224:38236 213.189.45.39:443 CLOSE_WAIT tcp 0 171 37.209.147.180:45341 173.194.113.146:443 ESTABLISHED tcp 0 1 37.209.147.180:52801 173.194.70.95:80 FIN_WAIT1 tcp 1 0 192.168.43.224:36080 93.184.220.148:80 CLOSE_WAIT tcp 0 1 37.209.147.180:59856 141.101.114.59:80 FIN_WAIT1 tcp 0 1 37.209.147.180:44096 198.252.206.16:80 FIN_WAIT1 tcp 0 1 93.154.166.62:57471 108.160.162.49:80 FIN_WAIT1 tcp 0 1 37.209.147.180:59854 141.101.114.59:80 FIN_WAIT1 tcp 0 171 37.209.147.180:45340 173.194.113.146:443 ESTABLISHED tcp 0 168 37.209.147.180:45334 173.194.113.146:443 FIN_WAIT1 tcp 1 0 93.154.146.186:46609 23.21.151.181:80 CLOSE_WAIT tcp 0 1248 93.154.166.62:58270 64.251.23.59:443 FIN_WAIT1 tcp 0 1 37.209.147.180:59850 141.101.114.59:80 FIN_WAIT1 tcp 1 0 93.154.146.186:35181 75.101.152.29:80 CLOSE_WAIT tcp 232 0 93.154.172.168:46384 198.252.206.25:80 ESTABLISHED tcp 1 0 93.154.146.186:52618 107.22.246.76:80 CLOSE_WAIT tcp 1 0 93.154.172.168:36298 173.194.69.95:443 CLOSE_WAIT tcp 1 0 93.154.146.186:60209 75.101.152.29:443 CLOSE_WAIT tcp 0 168 37.209.147.180:45335 173.194.113.146:443 FIN_WAIT1 tcp 415 0 93.154.146.186:36157 82.112.106.104:80 CLOSE_WAIT tcp 1 0 192.168.43.224:36082 93.184.220.148:80 CLOSE_WAIT tcp 1 0 192.168.43.224:32942 199.15.160.100:80 CLOSE_WAIT tcp 1 0 93.154.146.186:50350 107.22.246.76:443 CLOSE_WAIT tcp 1 0 192.168.43.224:32941 199.15.160.100:80 CLOSE_WAIT tcp 0 534 37.209.147.180:44089 198.252.206.16:80 FIN_WAIT1 tcp 1 0 93.154.146.186:46608 23.21.151.181:80 CLOSE_WAIT tcp 1 0 93.154.146.186:46612 23.21.151.181:80 CLOSE_WAIT udp 0 0 37.209.147.180:49057 193.41.112.14:53 ESTABLISHED udp 0 0 37.209.147.180:51631 193.41.112.18:53 ESTABLISHED udp 0 0 37.209.147.180:34827 193.41.112.18:53 ESTABLISHED udp 0 0 37.209.147.180:35908 193.41.112.14:53 ESTABLISHED udp 0 0 37.209.147.180:44106 193.41.112.14:53 ESTABLISHED udp 0 0 37.209.147.180:42184 193.41.112.14:53 ESTABLISHED udp 0 0 37.209.147.180:54485 193.41.112.14:53 ESTABLISHED udp 0 0 37.209.147.180:42216 193.41.112.18:53 ESTABLISHED udp 0 0 37.209.147.180:51961 193.41.112.14:53 ESTABLISHED udp 0 0 37.209.147.180:48412 193.41.112.14:53 ESTABLISHED The interesting lines from ping got lost, but the summary over past few hours is: --- 8.8.8.8 ping statistics --- 107459 packets transmitted, 104376 received, +22 duplicates, 2% packet loss, time 195427362ms rtt min/avg/max/mdev = 24.822/528.132/90538.257/2519.263 ms, pipe 90 EDIT: Per request: Happened again, reboot didn't help but cleaned up all "hanging" processes. Currently netstat shows: bw@pony:/var/log$ netstat -n -t Active Internet connections (w/o servers) Proto Recv-Q Send-Q Local Address Foreign Address State tcp 0 0 93.154.188.68:42767 74.125.239.143:443 TIME_WAIT tcp 0 0 93.154.188.68:50270 173.194.69.189:443 ESTABLISHED tcp 0 0 93.154.188.68:45250 190.93.244.58:80 TIME_WAIT tcp 0 0 93.154.188.68:53488 173.194.32.198:80 ESTABLISHED tcp 0 0 93.154.188.68:53490 173.194.32.198:80 ESTABLISHED tcp 0 159 93.154.188.68:42741 74.125.239.143:443 LAST_ACK tcp 0 0 93.154.188.68:45808 198.252.206.25:80 ESTABLISHED tcp 0 0 93.154.188.68:52449 173.194.32.199:443 ESTABLISHED tcp 0 0 93.154.188.68:52600 173.194.32.199:443 TIME_WAIT tcp 0 0 93.154.188.68:50300 173.194.69.189:443 TIME_WAIT tcp 0 0 93.154.188.68:45253 190.93.244.58:80 TIME_WAIT tcp 0 0 93.154.188.68:46252 173.194.32.204:443 ESTABLISHED tcp 0 0 93.154.188.68:45246 190.93.244.58:80 ESTABLISHED tcp 0 0 93.154.188.68:47064 173.194.113.143:443 ESTABLISHED tcp 0 0 93.154.188.68:34484 173.194.69.95:443 ESTABLISHED tcp 0 0 93.154.188.68:45252 190.93.244.58:80 TIME_WAIT tcp 0 0 93.154.188.68:54290 173.194.32.202:443 ESTABLISHED tcp 0 0 93.154.188.68:47063 173.194.113.143:443 ESTABLISHED tcp 0 0 93.154.188.68:53469 173.194.32.198:80 TIME_WAIT tcp 0 0 93.154.188.68:45242 190.93.244.58:80 TIME_WAIT tcp 0 0 93.154.188.68:53468 173.194.32.198:80 ESTABLISHED tcp 0 0 93.154.188.68:50299 173.194.69.189:443 TIME_WAIT tcp 0 0 93.154.188.68:42764 74.125.239.143:443 TIME_WAIT tcp 0 0 93.154.188.68:45256 190.93.244.58:80 TIME_WAIT tcp 0 0 93.154.188.68:58047 108.160.162.105:80 ESTABLISHED tcp 0 0 93.154.188.68:45249 190.93.244.58:80 TIME_WAIT tcp 0 0 93.154.188.68:50297 173.194.69.189:443 TIME_WAIT tcp 0 0 93.154.188.68:53470 173.194.32.198:80 ESTABLISHED tcp 0 0 93.154.188.68:34100 68.232.35.121:443 ESTABLISHED tcp 0 0 93.154.188.68:42758 74.125.239.143:443 ESTABLISHED tcp 0 0 93.154.188.68:42765 74.125.239.143:443 TIME_WAIT tcp 0 0 93.154.188.68:39000 173.194.69.95:80 TIME_WAIT tcp 0 0 93.154.188.68:50296 173.194.69.189:443 TIME_WAIT tcp 0 0 93.154.188.68:53467 173.194.32.198:80 ESTABLISHED tcp 0 0 93.154.188.68:42766 74.125.239.143:443 TIME_WAIT tcp 0 0 93.154.188.68:45251 190.93.244.58:80 TIME_WAIT tcp 0 0 93.154.188.68:45248 190.93.244.58:80 TIME_WAIT tcp 0 0 93.154.188.68:45247 190.93.244.58:80 ESTABLISHED tcp 0 159 93.154.188.68:50254 173.194.69.189:443 LAST_ACK tcp 0 0 93.154.188.68:34483 173.194.69.95:443 ESTABLISHED Output of ps: USER PID %CPU %MEM VSZ RSS TTY STAT START TIME COMMAND root 1 0.8 0.0 3628 2092 ? Ss 16:52 0:03 /sbin/init root 2 0.0 0.0 0 0 ? S 16:52 0:00 [kthreadd] root 3 0.1 0.0 0 0 ? S 16:52 0:00 [ksoftirqd/0] root 4 0.1 0.0 0 0 ? S 16:52 0:00 [kworker/0:0] root 6 0.0 0.0 0 0 ? S 16:52 0:00 [migration/0] root 7 0.0 0.0 0 0 ? S 16:52 0:00 [watchdog/0] root 8 0.0 0.0 0 0 ? S 16:52 0:00 [migration/1] root 10 0.1 0.0 0 0 ? S 16:52 0:00 [ksoftirqd/1] root 11 0.0 0.0 0 0 ? S 16:52 0:00 [watchdog/1] root 12 0.0 0.0 0 0 ? S 16:52 0:00 [migration/2] root 14 0.1 0.0 0 0 ? S 16:52 0:00 [ksoftirqd/2] root 15 0.0 0.0 0 0 ? S 16:52 0:00 [watchdog/2] root 16 0.0 0.0 0 0 ? S 16:52 0:00 [migration/3] root 17 0.0 0.0 0 0 ? S 16:52 0:00 [kworker/3:0] root 18 0.1 0.0 0 0 ? S 16:52 0:00 [ksoftirqd/3] root 19 0.0 0.0 0 0 ? S 16:52 0:00 [watchdog/3] root 20 0.0 0.0 0 0 ? S< 16:52 0:00 [cpuset] root 21 0.0 0.0 0 0 ? S< 16:52 0:00 [khelper] root 22 0.0 0.0 0 0 ? S 16:52 0:00 [kdevtmpfs] root 23 0.0 0.0 0 0 ? S< 16:52 0:00 [netns] root 24 0.0 0.0 0 0 ? S 16:52 0:00 [sync_supers] root 25 0.0 0.0 0 0 ? S 16:52 0:00 [bdi-default] root 26 0.0 0.0 0 0 ? S< 16:52 0:00 [kintegrityd] root 27 0.0 0.0 0 0 ? S< 16:52 0:00 [kblockd] root 28 0.0 0.0 0 0 ? S< 16:52 0:00 [ata_sff] root 29 0.0 0.0 0 0 ? S 16:52 0:00 [khubd] root 30 0.0 0.0 0 0 ? S< 16:52 0:00 [md] root 42 0.0 0.0 0 0 ? S 16:52 0:00 [khungtaskd] root 43 0.0 0.0 0 0 ? S 16:52 0:00 [kswapd0] root 44 0.0 0.0 0 0 ? SN 16:52 0:00 [ksmd] root 45 0.0 0.0 0 0 ? SN 16:52 0:00 [khugepaged] root 46 0.0 0.0 0 0 ? S 16:52 0:00 [fsnotify_mark] root 47 0.0 0.0 0 0 ? S 16:52 0:00 [ecryptfs-kthrea] root 48 0.0 0.0 0 0 ? S< 16:52 0:00 [crypto] root 59 0.0 0.0 0 0 ? S< 16:52 0:00 [kthrotld] root 70 0.1 0.0 0 0 ? S 16:52 0:00 [kworker/2:1] root 71 0.0 0.0 0 0 ? S 16:52 0:00 [scsi_eh_0] root 72 0.0 0.0 0 0 ? S 16:52 0:00 [scsi_eh_1] root 73 0.0 0.0 0 0 ? S 16:52 0:00 [scsi_eh_2] root 74 0.0 0.0 0 0 ? S 16:52 0:00 [scsi_eh_3] root 75 0.0 0.0 0 0 ? S 16:52 0:00 [kworker/u:2] root 76 0.0 0.0 0 0 ? S 16:52 0:00 [kworker/u:3] root 79 0.0 0.0 0 0 ? S 16:52 0:00 [kworker/1:1] root 99 0.0 0.0 0 0 ? S< 16:52 0:00 [deferwq] root 100 0.0 0.0 0 0 ? S< 16:52 0:00 [charger_manager] root 101 0.0 0.0 0 0 ? S< 16:52 0:00 [devfreq_wq] root 102 0.1 0.0 0 0 ? S 16:52 0:00 [kworker/2:2] root 106 0.0 0.0 0 0 ? S 16:52 0:00 [scsi_eh_4] root 107 0.0 0.0 0 0 ? S 16:52 0:00 [usb-storage] root 108 0.0 0.0 0 0 ? S 16:52 0:00 [scsi_eh_5] root 109 0.0 0.0 0 0 ? S 16:52 0:00 [usb-storage] root 271 0.1 0.0 0 0 ? S 16:52 0:00 [kworker/1:2] root 316 0.0 0.0 0 0 ? S 16:52 0:00 [jbd2/sda1-8] root 317 0.0 0.0 0 0 ? S< 16:52 0:00 [ext4-dio-unwrit] root 440 0.1 0.0 2820 608 ? S 16:52 0:00 upstart-udev-bridge --daemon root 478 0.0 0.0 3460 1648 ? Ss 16:52 0:00 /sbin/udevd --daemon root 632 0.0 0.0 3348 1336 ? S 16:52 0:00 /sbin/udevd --daemon root 633 0.0 0.0 3348 1204 ? S 16:52 0:00 /sbin/udevd --daemon root 782 0.0 0.0 2816 596 ? S 16:52 0:00 upstart-socket-bridge --daemon root 822 0.0 0.0 6684 2400 ? Ss 16:52 0:00 /usr/sbin/sshd -D 102 834 0.2 0.0 4064 1864 ? Ss 16:52 0:01 dbus-daemon --system --fork root 857 0.0 0.1 7420 3380 ? Ss 16:52 0:00 /usr/sbin/modem-manager root 858 0.0 0.0 4784 1636 ? Ss 16:52 0:00 /usr/sbin/bluetoothd syslog 860 0.0 0.0 31068 1496 ? Sl 16:52 0:00 rsyslogd -c5 root 869 0.1 0.1 24280 5564 ? Ssl 16:52 0:00 NetworkManager avahi 883 0.0 0.0 3448 1488 ? S 16:52 0:00 avahi-daemon: running [pony.local] avahi 884 0.0 0.0 3448 436 ? S 16:52 0:00 avahi-daemon: chroot helper root 885 0.0 0.0 0 0 ? S< 16:52 0:00 [kpsmoused] root 892 0.0 0.1 25696 4140 ? Sl 16:52 0:00 /usr/lib/policykit-1/polkitd --no-debug root 923 0.0 0.0 0 0 ? S 16:52 0:00 [scsi_eh_6] root 959 0.0 0.0 0 0 ? S< 16:52 0:00 [krfcommd] root 970 0.0 0.1 7536 3120 ? Ss 16:52 0:00 /usr/sbin/cupsd -F colord 976 0.1 0.3 55080 10396 ? Sl 16:52 0:00 /usr/lib/i386-linux-gnu/colord/colord root 979 0.0 0.0 4632 872 tty4 Ss+ 16:52 0:00 /sbin/getty -8 38400 tty4 root 987 0.0 0.0 4632 884 tty5 Ss+ 16:52 0:00 /sbin/getty -8 38400 tty5 root 994 0.0 0.0 4632 884 tty2 Ss+ 16:52 0:00 /sbin/getty -8 38400 tty2 root 995 0.0 0.0 4632 868 tty3 Ss+ 16:52 0:00 /sbin/getty -8 38400 tty3 root 998 0.0 0.0 4632 876 tty6 Ss+ 16:52 0:00 /sbin/getty -8 38400 tty6 root 1022 0.0 0.0 2176 680 ? Ss 16:52 0:00 acpid -c /etc/acpi/events -s /var/run/acpid.socket root 1029 0.0 0.0 3632 664 ? Ss 16:52 0:00 /usr/sbin/irqbalance daemon 1030 0.0 0.0 2476 120 ? Ss 16:52 0:00 atd root 1031 0.0 0.0 2620 880 ? Ss 16:52 0:00 cron root 1061 0.1 0.0 0 0 ? S 16:52 0:00 [kworker/3:2] root 1064 0.0 1.0 34116 31072 ? SLsl 16:52 0:00 lightdm root 1076 13.4 1.2 118688 37920 tty7 Ssl+ 16:52 0:55 /usr/bin/X :0 -core -auth /var/run/lightdm/root/:0 -nolisten tcp vt7 -novtswit root 1085 0.0 0.0 0 0 ? S 16:52 0:00 [rts_pstor] root 1087 0.0 0.0 0 0 ? S 16:52 0:00 [rtsx-polling] root 1095 0.0 0.0 0 0 ? S< 16:52 0:00 [cfg80211] root 1127 0.0 0.0 0 0 ? S 16:52 0:00 [flush-8:0] root 1130 0.0 0.0 6136 1824 ? Ss 16:52 0:00 /sbin/wpa_supplicant -B -P /run/sendsigs.omit.d/wpasupplicant.pid -u -s -O /va root 1137 0.0 0.1 24604 3164 ? Sl 16:52 0:00 /usr/lib/accountsservice/accounts-daemon root 1140 0.0 0.0 0 0 ? S< 16:52 0:00 [hd-audio0] root 1188 0.0 0.1 34308 3420 ? Sl 16:52 0:00 /usr/sbin/console-kit-daemon --no-daemon root 1425 0.0 0.0 4632 872 tty1 Ss+ 16:52 0:00 /sbin/getty -8 38400 tty1 root 1443 0.1 0.1 29460 4664 ? Sl 16:52 0:00 /usr/lib/upower/upowerd root 1579 0.0 0.1 16540 3272 ? Sl 16:53 0:00 lightdm --session-child 12 19 bw 1623 0.0 0.0 2232 644 ? Ss 16:53 0:00 /bin/sh /usr/bin/startkde bw 1672 0.0 0.0 4092 204 ? Ss 16:53 0:00 /usr/bin/ssh-agent /usr/bin/gpg-agent --daemon --sh --write-env-file=/home/bw/ bw 1673 0.0 0.0 5492 384 ? Ss 16:53 0:00 /usr/bin/gpg-agent --daemon --sh --write-env-file=/home/bw/.gnupg/gpg-agent-in bw 1676 0.0 0.0 3848 792 ? S 16:53 0:00 /usr/bin/dbus-launch --exit-with-session /usr/bin/startkde bw 1677 0.5 0.0 5384 2180 ? Ss 16:53 0:02 //bin/dbus-daemon --fork --print-pid 5 --print-address 7 --session root 1704 0.3 0.1 25348 3600 ? Sl 16:53 0:01 /usr/lib/udisks/udisks-daemon root 1705 0.0 0.0 6620 728 ? S 16:53 0:00 udisks-daemon: not polling any devices bw 1736 0.0 0.0 2008 64 ? S 16:53 0:00 /usr/lib/kde4/libexec/start_kdeinit +kcminit_startup bw 1737 0.0 0.5 115200 15588 ? Ss 16:53 0:00 kdeinit4: kdeinit4 Running... bw 1738 0.1 0.2 116756 8728 ? S 16:53 0:00 kdeinit4: klauncher [kdeinit] --fd=9 bw 1740 0.6 1.0 340524 31264 ? Sl 16:53 0:02 kdeinit4: kded4 [kdeinit] bw 1742 0.0 0.0 8944 2144 ? S 16:53 0:00 /usr/lib/i386-linux-gnu/gconf/gconfd-2 bw 1746 0.2 0.4 92028 14688 ? S 16:53 0:00 /usr/bin/kglobalaccel bw 1748 0.0 0.4 90804 13500 ? S 16:53 0:00 /usr/bin/kwalletd bw 1752 0.1 0.5 103764 15152 ? S 16:53 0:00 /usr/bin/kactivitymanagerd bw 1758 0.0 0.0 2144 280 ? S 16:53 0:00 kwrapper4 ksmserver bw 1759 0.1 0.5 150016 16088 ? Sl 16:53 0:00 kdeinit4: ksmserver [kdeinit] bw 1763 2.2 1.0 178492 32100 ? Sl 16:53 0:08 kwin bw 1772 0.2 0.5 106292 16340 ? Sl 16:53 0:00 /usr/bin/knotify4 bw 1777 0.9 1.1 246120 32912 ? Sl 16:53 0:03 /usr/bin/krunner bw 1778 6.3 2.7 389884 80216 ? Sl 16:53 0:23 /usr/bin/plasma-desktop bw 1785 0.0 0.0 2844 1208 ? S 16:53 0:00 ksysguardd bw 1789 0.1 0.4 82036 14176 ? S 16:53 0:00 /usr/bin/kuiserver bw 1805 0.3 0.1 61560 5612 ? Sl 16:53 0:01 /usr/bin/akonadi_control root 1806 0.0 0.0 0 0 ? S 16:53 0:00 [kworker/0:2] bw 1808 0.1 0.2 211852 8460 ? Sl 16:53 0:00 akonadiserver bw 1810 0.4 0.8 244116 25360 ? Sl 16:53 0:01 /usr/sbin/mysqld --defaults-file=/home/bw/.local/share/akonadi/mysql.conf --da bw 1874 0.0 0.0 35284 2956 ? Sl 16:53 0:00 /usr/bin/xsettings-kde bw 1876 0.0 0.3 68776 9488 ? Sl 16:53 0:00 /usr/bin/nepomukserver bw 1884 0.4 0.9 173876 29240 ? SNl 16:53 0:01 /usr/bin/nepomukservicestub nepomukstorage bw 1902 6.1 2.1 451512 63924 ? Sl 16:53 0:21 /home/bw/.dropbox-dist/dropbox bw 1906 3.8 1.0 142368 32376 ? Rl 16:53 0:13 /usr/bin/yakuake bw 1933 0.0 0.1 54636 4680 ? Sl 16:53 0:00 /usr/bin/zeitgeist-datahub bw 1943 0.5 1.5 164836 46836 ? Sl 16:53 0:01 python /usr/bin/printer-applet bw 1945 0.1 0.1 99636 5048 ? S<l 16:53 0:00 /usr/bin/pulseaudio --start --log-target=syslog rtkit 1947 0.0 0.0 21336 1248 ? SNl 16:53 0:00 /usr/lib/rtkit/rtkit-daemon bw 1958 0.0 0.1 44204 3792 ? Sl 16:53 0:00 /usr/bin/zeitgeist-daemon bw 1972 0.0 0.0 27008 2684 ? Sl 16:53 0:00 /usr/lib/gvfs/gvfsd bw 1974 0.1 0.5 90480 16660 ? Sl 16:53 0:00 /usr/bin/akonadi_agent_launcher akonadi_akonotes_resource akonadi_akonotes_res bw 1984 0.1 0.5 90472 16636 ? Sl 16:53 0:00 /usr/bin/akonadi_agent_launcher akonadi_akonotes_resource akonadi_akonotes_res bw 1985 0.3 0.9 148800 28304 ? S 16:53 0:01 /usr/bin/akonadi_archivemail_agent --identifier akonadi_archivemail_agent bw 1992 0.1 0.5 90020 16148 ? Sl 16:53 0:00 /usr/bin/akonadi_agent_launcher akonadi_contacts_resource akonadi_contacts_res bw 1993 0.1 0.5 90132 16452 ? Sl 16:53 0:00 /usr/bin/akonadi_agent_launcher akonadi_contacts_resource akonadi_contacts_res bw 1994 0.1 0.5 90564 16332 ? Sl 16:53 0:00 /usr/bin/akonadi_agent_launcher akonadi_ical_resource akonadi_ical_resource_0 bw 1995 0.1 0.5 90676 16732 ? Sl 16:53 0:00 /usr/bin/akonadi_agent_launcher akonadi_ical_resource akonadi_ical_resource_1 bw 1996 0.1 0.5 90468 16800 ? Sl 16:53 0:00 /usr/bin/akonadi_agent_launcher akonadi_maildir_resource akonadi_maildir_resou bw 1999 0.2 0.6 99324 19276 ? S 16:53 0:00 /usr/bin/akonadi_maildispatcher_agent --identifier akonadi_maildispatcher_agen bw 2006 0.3 0.9 148808 28332 ? S 16:53 0:01 /usr/bin/akonadi_mailfilter_agent --identifier akonadi_mailfilter_agent bw 2017 0.0 0.1 50256 4716 ? Sl 16:53 0:00 /usr/lib/zeitgeist/zeitgeist-fts bw 2024 0.2 0.6 103632 18376 ? Sl 16:53 0:00 /usr/bin/akonadi_nepomuk_feeder --identifier akonadi_nepomuk_feeder bw 2043 0.0 0.0 4484 280 ? S 16:53 0:00 /bin/cat bw 2101 0.2 0.7 113600 22396 ? Sl 16:53 0:00 /usr/lib/kde4/libexec/polkit-kde-authentication-agent-1 bw 2105 0.2 0.7 114196 22072 ? Sl 16:53 0:00 /usr/bin/nepomukcontroller bw 2156 0.3 1.0 333188 31244 ? Sl 16:54 0:01 /usr/bin/kmix bw 2167 0.0 0.0 6548 2724 pts/2 Ss 16:54 0:00 /bin/bash bw 2177 0.2 0.7 113496 22960 ? Sl 16:54 0:00 /usr/bin/klipper bw 2394 3.5 1.2 52932 35596 ? SNl 16:54 0:11 /usr/bin/virtuoso-t +foreground +configfile /tmp/virtuoso_hX1884.ini +wait root 2460 0.0 0.0 6184 1876 pts/2 S 16:54 0:00 sudo -s root 2500 0.0 0.0 6528 2700 pts/2 S 16:54 0:00 /bin/bash root 2599 0.0 0.0 5444 1280 pts/2 S+ 16:54 0:00 /bin/bash bin/aero root 2606 0.1 0.0 9836 2500 pts/2 S+ 16:54 0:00 wvdial aero2 root 2619 0.0 0.0 3504 1280 pts/2 S 16:54 0:00 /usr/sbin/pppd 57600 modem crtscts defaultroute usehostname -detach user aero bw 2653 0.0 0.0 6600 2880 pts/3 Ss 16:54 0:00 /bin/bash bw 2676 0.4 0.8 130296 24016 ? SNl 16:54 0:01 /usr/bin/nepomukservicestub nepomukfilewatch bw 2679 0.1 0.7 101636 22252 ? SNl 16:54 0:00 /usr/bin/nepomukservicestub nepomukqueryservice bw 2681 0.2 0.8 109836 24280 ? SNl 16:54 0:00 /usr/bin/nepomukservicestub nepomukbackupsync bw 3833 46.0 9.7 829272 288012 ? Rl 16:55 1:46 /usr/lib/firefox/firefox bw 3903 0.0 0.0 35128 2804 ? Sl 16:55 0:00 /usr/lib/at-spi2-core/at-spi-bus-launcher bw 4708 0.1 0.0 6564 2736 pts/4 Ss 16:56 0:00 /bin/bash root 5210 0.0 0.0 0 0 ? S 16:57 0:00 [kworker/u:0] root 6140 0.2 0.0 0 0 ? S 16:58 0:00 [kworker/0:1] root 6371 0.5 0.0 6184 1868 pts/4 S+ 16:59 0:00 sudo nethogs ppp0 root 6411 17.7 0.2 8616 6144 pts/4 S+ 16:59 0:05 nethogs ppp0 bw 6787 0.0 0.0 5464 1220 pts/3 R+ 16:59 0:00 ps auxw

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Using R to Analyze G1GC Log Files

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Using R to Analyze G1GC Log Files Using R to Analyze G1GC Log Files Introduction Working in Oracle Platform Integration gives an engineer opportunities to work on a wide array of technologies. My team’s goal is to make Oracle applications run best on the Solaris/SPARC platform. When looking for bottlenecks in a modern applications, one needs to be aware of not only how the CPUs and operating system are executing, but also network, storage, and in some cases, the Java Virtual Machine. I was recently presented with about 1.5 GB of Java Garbage First Garbage Collector log file data. If you’re not familiar with the subject, you might want to review Garbage First Garbage Collector Tuning by Monica Beckwith. The customer had been running Java HotSpot 1.6.0_31 to host a web application server. I was told that the Solaris/SPARC server was running a Java process launched using a commmand line that included the following flags: -d64 -Xms9g -Xmx9g -XX:+UseG1GC -XX:MaxGCPauseMillis=200 -XX:InitiatingHeapOccupancyPercent=80 -XX:PermSize=256m -XX:MaxPermSize=256m -XX:+PrintGC -XX:+PrintGCTimeStamps -XX:+PrintHeapAtGC -XX:+PrintGCDateStamps -XX:+PrintFlagsFinal -XX:+DisableExplicitGC -XX:+UnlockExperimentalVMOptions -XX:ParallelGCThreads=8 Several sources on the internet indicate that if I were to print out the 1.5 GB of log files, it would require enough paper to fill the bed of a pick up truck. Of course, it would be fruitless to try to scan the log files by hand. Tools will be required to summarize the contents of the log files. Others have encountered large Java garbage collection log files. There are existing tools to analyze the log files: IBM’s GC toolkit The chewiebug GCViewer gchisto HPjmeter Instead of using one of the other tools listed, I decide to parse the log files with standard Unix tools, and analyze the data with R. Data Cleansing The log files arrived in two different formats. I guess that the difference is that one set of log files was generated using a more verbose option, maybe -XX:+PrintHeapAtGC, and the other set of log files was generated without that option. Format 1 In some of the log files, the log files with the less verbose format, a single trace, i.e. the report of a singe garbage collection event, looks like this: {Heap before GC invocations=12280 (full 61): garbage-first heap total 9437184K, used 7499918K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) region size 4096K, 1 young (4096K), 0 survivors (0K) compacting perm gen total 262144K, used 144077K [0xffffffff40000000, 0xffffffff50000000, 0xffffffff50000000) the space 262144K, 54% used [0xffffffff40000000, 0xffffffff48cb3758, 0xffffffff48cb3800, 0xffffffff50000000) No shared spaces configured. 2014-05-14T07:24:00.988-0700: 60586.353: [GC pause (young) 7324M->7320M(9216M), 0.1567265 secs] Heap after GC invocations=12281 (full 61): garbage-first heap total 9437184K, used 7496533K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) region size 4096K, 0 young (0K), 0 survivors (0K) compacting perm gen total 262144K, used 144077K [0xffffffff40000000, 0xffffffff50000000, 0xffffffff50000000) the space 262144K, 54% used [0xffffffff40000000, 0xffffffff48cb3758, 0xffffffff48cb3800, 0xffffffff50000000) No shared spaces configured. } A simple grep can be used to extract a summary: $ grep "\[ GC pause (young" g1gc.log 2014-05-13T13:24:35.091-0700: 3.109: [GC pause (young) 20M->5029K(9216M), 0.0146328 secs] 2014-05-13T13:24:35.440-0700: 3.459: [GC pause (young) 9125K->6077K(9216M), 0.0086723 secs] 2014-05-13T13:24:37.581-0700: 5.599: [GC pause (young) 25M->8470K(9216M), 0.0203820 secs] 2014-05-13T13:24:42.686-0700: 10.704: [GC pause (young) 44M->15M(9216M), 0.0288848 secs] 2014-05-13T13:24:48.941-0700: 16.958: [GC pause (young) 51M->20M(9216M), 0.0491244 secs] 2014-05-13T13:24:56.049-0700: 24.066: [GC pause (young) 92M->26M(9216M), 0.0525368 secs] 2014-05-13T13:25:34.368-0700: 62.383: [GC pause (young) 602M->68M(9216M), 0.1721173 secs] But that format wasn't easily read into R, so I needed to be a bit more tricky. I used the following Unix command to create a summary file that was easy for R to read. $ echo "SecondsSinceLaunch BeforeSize AfterSize TotalSize RealTime" $ grep "\[GC pause (young" g1gc.log | grep -v mark | sed -e 's/[A-SU-z,]/ /g' -e 's/->/ /' -e 's/: / /g' | more SecondsSinceLaunch BeforeSize AfterSize TotalSize RealTime 2014-05-13T13:24:35.091-0700 3.109 20 5029 9216 0.0146328 2014-05-13T13:24:35.440-0700 3.459 9125 6077 9216 0.0086723 2014-05-13T13:24:37.581-0700 5.599 25 8470 9216 0.0203820 2014-05-13T13:24:42.686-0700 10.704 44 15 9216 0.0288848 2014-05-13T13:24:48.941-0700 16.958 51 20 9216 0.0491244 2014-05-13T13:24:56.049-0700 24.066 92 26 9216 0.0525368 2014-05-13T13:25:34.368-0700 62.383 602 68 9216 0.1721173 Format 2 In some of the log files, the log files with the more verbose format, a single trace, i.e. the report of a singe garbage collection event, was more complicated than Format 1. Here is a text file with an example of a single G1GC trace in the second format. As you can see, it is quite complicated. It is nice that there is so much information available, but the level of detail can be overwhelming. I wrote this awk script (download) to summarize each trace on a single line. #!/usr/bin/env awk -f BEGIN { printf("SecondsSinceLaunch IncrementalCount FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize\n") } ###################### # Save count data from lines that are at the start of each G1GC trace. # Each trace starts out like this: # {Heap before GC invocations=14 (full 0): # garbage-first heap total 9437184K, used 325496K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) ###################### /{Heap.*full/{ gsub ( "\\)" , "" ); nf=split($0,a,"="); split(a[2],b," "); getline; if ( match($0, "first") ) { G1GC=1; IncrementalCount=b[1]; FullCount=substr( b[3], 1, length(b[3])-1 ); } else { G1GC=0; } } ###################### # Pull out time stamps that are in lines with this format: # 2014-05-12T14:02:06.025-0700: 94.312: [GC pause (young), 0.08870154 secs] ###################### /GC pause/ { DateTime=$1; SecondsSinceLaunch=substr($2, 1, length($2)-1); } ###################### # Heap sizes are in lines that look like this: # [ 4842M->4838M(9216M)] ###################### /\[ .*]$/ { gsub ( "\\[" , "" ); gsub ( "\ \]" , "" ); gsub ( "->" , " " ); gsub ( "\$ " , " " ); gsub ( "\ $" , " " ); split($0,a," "); if ( split(a[1],b,"M") > 1 ) {BeforeSize=b[1]*1024;} if ( split(a[1],b,"K") > 1 ) {BeforeSize=b[1];} if ( split(a[2],b,"M") > 1 ) {AfterSize=b[1]*1024;} if ( split(a[2],b,"K") > 1 ) {AfterSize=b[1];} if ( split(a[3],b,"M") > 1 ) {TotalSize=b[1]*1024;} if ( split(a[3],b,"K") > 1 ) {TotalSize=b[1];} } ###################### # Emit an output line when you find input that looks like this: # [Times: user=1.41 sys=0.08, real=0.24 secs] ###################### /\[Times/ { if (G1GC==1) { gsub ( "," , "" ); split($2,a,"="); UserTime=a[2]; split($3,a,"="); SysTime=a[2]; split($4,a,"="); RealTime=a[2]; print DateTime,SecondsSinceLaunch,IncrementalCount,FullCount,UserTime,SysTime,RealTime,BeforeSize,AfterSize,TotalSize; G1GC=0; } } The resulting summary is about 25X smaller that the original file, but still difficult for a human to digest. SecondsSinceLaunch IncrementalCount FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ... 2014-05-12T18:36:34.669-0700: 3985.744 561 0 0.57 0.06 0.16 1724416 1720320 9437184 2014-05-12T18:36:34.839-0700: 3985.914 562 0 0.51 0.06 0.19 1724416 1720320 9437184 2014-05-12T18:36:35.069-0700: 3986.144 563 0 0.60 0.04 0.27 1724416 1721344 9437184 2014-05-12T18:36:35.354-0700: 3986.429 564 0 0.33 0.04 0.09 1725440 1722368 9437184 2014-05-12T18:36:35.545-0700: 3986.620 565 0 0.58 0.04 0.17 1726464 1722368 9437184 2014-05-12T18:36:35.726-0700: 3986.801 566 0 0.43 0.05 0.12 1726464 1722368 9437184 2014-05-12T18:36:35.856-0700: 3986.930 567 0 0.30 0.04 0.07 1726464 1723392 9437184 2014-05-12T18:36:35.947-0700: 3987.023 568 0 0.61 0.04 0.26 1727488 1723392 9437184 2014-05-12T18:36:36.228-0700: 3987.302 569 0 0.46 0.04 0.16 1731584 1724416 9437184 Reading the Data into R Once the GC log data had been cleansed, either by processing the first format with the shell script, or by processing the second format with the awk script, it was easy to read the data into R. g1gc.df = read.csv("summary.txt", row.names = NULL, stringsAsFactors=FALSE,sep="") str(g1gc.df) ## 'data.frame': 8307 obs. of 10 variables: ## $ row.names : chr "2014-05-12T14:00:32.868-0700:" "2014-05-12T14:00:33.179-0700:" "2014-05-12T14:00:33.677-0700:" "2014-05-12T14:00:35.538-0700:" ... ## $ SecondsSinceLaunch: num 1.16 1.47 1.97 3.83 6.1 ... ## $ IncrementalCount : int 0 1 2 3 4 5 6 7 8 9 ... ## $ FullCount : int 0 0 0 0 0 0 0 0 0 0 ... ## $ UserTime : num 0.11 0.05 0.04 0.21 0.08 0.26 0.31 0.33 0.34 0.56 ... ## $ SysTime : num 0.04 0.01 0.01 0.05 0.01 0.06 0.07 0.06 0.07 0.09 ... ## $ RealTime : num 0.02 0.02 0.01 0.04 0.02 0.04 0.05 0.04 0.04 0.06 ... ## $ BeforeSize : int 8192 5496 5768 22528 24576 43008 34816 53248 55296 93184 ... ## $ AfterSize : int 1400 1672 2557 4907 7072 14336 16384 18432 19456 21504 ... ## $ TotalSize : int 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 ... head(g1gc.df) ## row.names SecondsSinceLaunch IncrementalCount ## 1 2014-05-12T14:00:32.868-0700: 1.161 0 ## 2 2014-05-12T14:00:33.179-0700: 1.472 1 ## 3 2014-05-12T14:00:33.677-0700: 1.969 2 ## 4 2014-05-12T14:00:35.538-0700: 3.830 3 ## 5 2014-05-12T14:00:37.811-0700: 6.103 4 ## 6 2014-05-12T14:00:41.428-0700: 9.720 5 ## FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ## 1 0 0.11 0.04 0.02 8192 1400 9437184 ## 2 0 0.05 0.01 0.02 5496 1672 9437184 ## 3 0 0.04 0.01 0.01 5768 2557 9437184 ## 4 0 0.21 0.05 0.04 22528 4907 9437184 ## 5 0 0.08 0.01 0.02 24576 7072 9437184 ## 6 0 0.26 0.06 0.04 43008 14336 9437184 Basic Statistics Once the data has been read into R, simple statistics are very easy to generate. All of the numbers from high school statistics are available via simple commands. For example, generate a summary of every column: summary(g1gc.df) ## row.names SecondsSinceLaunch IncrementalCount FullCount ## Length:8307 Min. : 1 Min. : 0 Min. : 0.0 ## Class :character 1st Qu.: 9977 1st Qu.:2048 1st Qu.: 0.0 ## Mode :character Median :12855 Median :4136 Median : 12.0 ## Mean :12527 Mean :4156 Mean : 31.6 ## 3rd Qu.:15758 3rd Qu.:6262 3rd Qu.: 61.0 ## Max. :55484 Max. :8391 Max. :113.0 ## UserTime SysTime RealTime BeforeSize ## Min. :0.040 Min. :0.0000 Min. : 0.0 Min. : 5476 ## 1st Qu.:0.470 1st Qu.:0.0300 1st Qu.: 0.1 1st Qu.:5137920 ## Median :0.620 Median :0.0300 Median : 0.1 Median :6574080 ## Mean :0.751 Mean :0.0355 Mean : 0.3 Mean :5841855 ## 3rd Qu.:0.920 3rd Qu.:0.0400 3rd Qu.: 0.2 3rd Qu.:7084032 ## Max. :3.370 Max. :1.5600 Max. :488.1 Max. :8696832 ## AfterSize TotalSize ## Min. : 1380 Min. :9437184 ## 1st Qu.:5002752 1st Qu.:9437184 ## Median :6559744 Median :9437184 ## Mean :5785454 Mean :9437184 ## 3rd Qu.:7054336 3rd Qu.:9437184 ## Max. :8482816 Max. :9437184 Q: What is the total amount of User CPU time spent in garbage collection? sum(g1gc.df$UserTime) ## [1] 6236 As you can see, less than two hours of CPU time was spent in garbage collection. Is that too much? To find the percentage of time spent in garbage collection, divide the number above by total_elapsed_time*CPU_count. In this case, there are a lot of CPU’s and it turns out the the overall amount of CPU time spent in garbage collection isn’t a problem when viewed in isolation. When calculating rates, i.e. events per unit time, you need to ask yourself if the rate is homogenous across the time period in the log file. Does the log file include spikes of high activity that should be separately analyzed? Averaging in data from nights and weekends with data from business hours may alias problems. If you have a reason to suspect that the garbage collection rates include peaks and valleys that need independent analysis, see the “Time Series” section, below. Q: How much garbage is collected on each pass? The amount of heap space that is recovered per GC pass is surprisingly low: At least one collection didn’t recover any data. (“Min.=0”) 25% of the passes recovered 3MB or less. (“1st Qu.=3072”) Half of the GC passes recovered 4MB or less. (“Median=4096”) The average amount recovered was 56MB. (“Mean=56390”) 75% of the passes recovered 36MB or less. (“3rd Qu.=36860”) At least one pass recovered 2GB. (“Max.=2121000”) g1gc.df$Delta = g1gc.df$BeforeSize - g1gc.df$AfterSize summary(g1gc.df$Delta) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0 3070 4100 56400 36900 2120000 Q: What is the maximum User CPU time for a single collection? The worst garbage collection (“Max.”) is many standard deviations away from the mean. The data appears to be right skewed. summary(g1gc.df$UserTime) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0.040 0.470 0.620 0.751 0.920 3.370 sd(g1gc.df$UserTime) ## [1] 0.3966 Basic Graphics Once the data is in R, it is trivial to plot the data with formats including dot plots, line charts, bar charts (simple, stacked, grouped), pie charts, boxplots, scatter plots histograms, and kernel density plots. Histogram of User CPU Time per Collection I don't think that this graph requires any explanation. hist(g1gc.df$UserTime, main="User CPU Time per Collection", xlab="Seconds", ylab="Frequency") Box plot to identify outliers When the initial data is viewed with a box plot, you can see the one crazy outlier in the real time per GC. Save this data point for future analysis and drop the outlier so that it’s not throwing off our statistics. Now the box plot shows many outliers, which will be examined later, using times series analysis. Notice that the scale of the x-axis changes drastically once the crazy outlier is removed. par(mfrow=c(2,1)) boxplot(g1gc.df$UserTime,g1gc.df$SysTime,g1gc.df$RealTime, main="Box Plot of Time per GC\n(dominated by a crazy outlier)", names=c("usr","sys","elapsed"), xlab="Seconds per GC", ylab="Time (Seconds)", horizontal = TRUE, outcol="red") crazy.outlier.df=g1gc.df[g1gc.df$RealTime > 400,] g1gc.df=g1gc.df[g1gc.df$RealTime < 400,] boxplot(g1gc.df$UserTime,g1gc.df$SysTime,g1gc.df$RealTime, main="Box Plot of Time per GC\n(crazy outlier excluded)", names=c("usr","sys","elapsed"), xlab="Seconds per GC", ylab="Time (Seconds)", horizontal = TRUE, outcol="red") box(which = "outer", lty = "solid") Here is the crazy outlier for future analysis: crazy.outlier.df ## row.names SecondsSinceLaunch IncrementalCount ## 8233 2014-05-12T23:15:43.903-0700: 20741 8316 ## FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ## 8233 112 0.55 0.42 488.1 8381440 8235008 9437184 ## Delta ## 8233 146432 R Time Series Data To analyze the garbage collection as a time series, I’ll use Z’s Ordered Observations (zoo). “zoo is the creator for an S3 class of indexed totally ordered observations which includes irregular time series.” require(zoo) ## Loading required package: zoo ## ## Attaching package: 'zoo' ## ## The following objects are masked from 'package:base': ## ## as.Date, as.Date.numeric head(g1gc.df[,1]) ## [1] "2014-05-12T14:00:32.868-0700:" "2014-05-12T14:00:33.179-0700:" ## [3] "2014-05-12T14:00:33.677-0700:" "2014-05-12T14:00:35.538-0700:" ## [5] "2014-05-12T14:00:37.811-0700:" "2014-05-12T14:00:41.428-0700:" options("digits.secs"=3) times=as.POSIXct( g1gc.df[,1], format="%Y-%m-%dT%H:%M:%OS%z:") g1gc.z = zoo(g1gc.df[,-c(1)], order.by=times) head(g1gc.z) ## SecondsSinceLaunch IncrementalCount FullCount ## 2014-05-12 17:00:32.868 1.161 0 0 ## 2014-05-12 17:00:33.178 1.472 1 0 ## 2014-05-12 17:00:33.677 1.969 2 0 ## 2014-05-12 17:00:35.538 3.830 3 0 ## 2014-05-12 17:00:37.811 6.103 4 0 ## 2014-05-12 17:00:41.427 9.720 5 0 ## UserTime SysTime RealTime BeforeSize AfterSize ## 2014-05-12 17:00:32.868 0.11 0.04 0.02 8192 1400 ## 2014-05-12 17:00:33.178 0.05 0.01 0.02 5496 1672 ## 2014-05-12 17:00:33.677 0.04 0.01 0.01 5768 2557 ## 2014-05-12 17:00:35.538 0.21 0.05 0.04 22528 4907 ## 2014-05-12 17:00:37.811 0.08 0.01 0.02 24576 7072 ## 2014-05-12 17:00:41.427 0.26 0.06 0.04 43008 14336 ## TotalSize Delta ## 2014-05-12 17:00:32.868 9437184 6792 ## 2014-05-12 17:00:33.178 9437184 3824 ## 2014-05-12 17:00:33.677 9437184 3211 ## 2014-05-12 17:00:35.538 9437184 17621 ## 2014-05-12 17:00:37.811 9437184 17504 ## 2014-05-12 17:00:41.427 9437184 28672 Example of Two Benchmark Runs in One Log File The data in the following graph is from a different log file, not the one of primary interest to this article. I’m including this image because it is an example of idle periods followed by busy periods. It would be uninteresting to average the rate of garbage collection over the entire log file period. More interesting would be the rate of garbage collect in the two busy periods. Are they the same or different? Your production data may be similar, for example, bursts when employees return from lunch and idle times on weekend evenings, etc. Once the data is in an R Time Series, you can analyze isolated time windows. Clipping the Time Series data Flashing back to our test case… Viewing the data as a time series is interesting. You can see that the work intensive time period is between 9:00 PM and 3:00 AM. Lets clip the data to the interesting period: par(mfrow=c(2,1)) plot(g1gc.z$UserTime, type="h", main="User Time per GC\nTime: Complete Log File", xlab="Time of Day", ylab="CPU Seconds per GC", col="#1b9e77") clipped.g1gc.z=window(g1gc.z, start=as.POSIXct("2014-05-12 21:00:00"), end=as.POSIXct("2014-05-13 03:00:00")) plot(clipped.g1gc.z$UserTime, type="h", main="User Time per GC\nTime: Limited to Benchmark Execution", xlab="Time of Day", ylab="CPU Seconds per GC", col="#1b9e77") box(which = "outer", lty = "solid") Cumulative Incremental and Full GC count Here is the cumulative incremental and full GC count. When the line is very steep, it indicates that the GCs are repeating very quickly. Notice that the scale on the Y axis is different for full vs. incremental. plot(clipped.g1gc.z[,c(2:3)], main="Cumulative Incremental and Full GC count", xlab="Time of Day", col="#1b9e77") GC Analysis of Benchmark Execution using Time Series data In the following series of 3 graphs: The “After Size” show the amount of heap space in use after each garbage collection. Many Java objects are still referenced, i.e. alive, during each garbage collection. This may indicate that the application has a memory leak, or may indicate that the application has a very large memory footprint. Typically, an application's memory footprint plateau's in the early stage of execution. One would expect this graph to have a flat top. The steep decline in the heap space may indicate that the application crashed after 2:00. The second graph shows that the outliers in real execution time, discussed above, occur near 2:00. when the Java heap seems to be quite full. The third graph shows that Full GCs are infrequent during the first few hours of execution. The rate of Full GC's, (the slope of the cummulative Full GC line), changes near midnight. plot(clipped.g1gc.z[,c("AfterSize","RealTime","FullCount")], xlab="Time of Day", col=c("#1b9e77","red","#1b9e77")) GC Analysis of heap recovered Each GC trace includes the amount of heap space in use before and after the individual GC event. During garbage coolection, unreferenced objects are identified, the space holding the unreferenced objects is freed, and thus, the difference in before and after usage indicates how much space has been freed. The following box plot and bar chart both demonstrate the same point - the amount of heap space freed per garbage colloection is surprisingly low. par(mfrow=c(2,1)) boxplot(as.vector(clipped.g1gc.z$Delta), main="Amount of Heap Recovered per GC Pass", xlab="Size in KB", horizontal = TRUE, col="red") hist(as.vector(clipped.g1gc.z$Delta), main="Amount of Heap Recovered per GC Pass", xlab="Size in KB", breaks=100, col="red") box(which = "outer", lty = "solid") This graph is the most interesting. The dark blue area shows how much heap is occupied by referenced Java objects. This represents memory that holds live data. The red fringe at the top shows how much data was recovered after each garbage collection. barplot(clipped.g1gc.z[,c("AfterSize","Delta")], col=c("#7570b3","#e7298a"), xlab="Time of Day", border=NA) legend("topleft", c("Live Objects","Heap Recovered on GC"), fill=c("#7570b3","#e7298a")) box(which = "outer", lty = "solid") When I discuss the data in the log files with the customer, I will ask for an explaination for the large amount of referenced data resident in the Java heap. There are two are posibilities: There is a memory leak and the amount of space required to hold referenced objects will continue to grow, limited only by the maximum heap size. After the maximum heap size is reached, the JVM will throw an “Out of Memory” exception every time that the application tries to allocate a new object. If this is the case, the aplication needs to be debugged to identify why old objects are referenced when they are no longer needed. The application has a legitimate requirement to keep a large amount of data in memory. The customer may want to further increase the maximum heap size. Another possible solution would be to partition the application across multiple cluster nodes, where each node has responsibility for managing a unique subset of the data. Conclusion In conclusion, R is a very powerful tool for the analysis of Java garbage collection log files. The primary difficulty is data cleansing so that information can be read into an R data frame. Once the data has been read into R, a rich set of tools may be used for thorough evaluation.

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How John Got 15x Improvement Without Really Trying

- by rchrd

The following article was published on a Sun Microsystems website a number of years ago by John Feo. It is still useful and worth preserving. So I'm republishing it here. How I Got 15x Improvement Without Really Trying John Feo, Sun Microsystems Taking ten "personal" program codes used in scientific and engineering research, the author was able to get from 2 to 15 times performance improvement easily by applying some simple general optimization techniques. Introduction Scientific research based on computer simulation depends on the simulation for advancement. The research can advance only as fast as the computational codes can execute. The codes' efficiency determines both the rate and quality of results. In the same amount of time, a faster program can generate more results and can carry out a more detailed simulation of physical phenomena than a slower program. Highly optimized programs help science advance quickly and insure that monies supporting scientific research are used as effectively as possible. Scientific computer codes divide into three broad categories: ISV, community, and personal. ISV codes are large, mature production codes developed and sold commercially. The codes improve slowly over time both in methods and capabilities, and they are well tuned for most vendor platforms. Since the codes are mature and complex, there are few opportunities to improve their performance solely through code optimization. Improvements of 10% to 15% are typical. Examples of ISV codes are DYNA3D, Gaussian, and Nastran. Community codes are non-commercial production codes used by a particular research field. Generally, they are developed and distributed by a single academic or research institution with assistance from the community. Most users just run the codes, but some develop new methods and extensions that feed back into the general release. The codes are available on most vendor platforms. Since these codes are younger than ISV codes, there are more opportunities to optimize the source code. Improvements of 50% are not unusual. Examples of community codes are AMBER, CHARM, BLAST, and FASTA. Personal codes are those written by single users or small research groups for their own use. These codes are not distributed, but may be passed from professor-to-student or student-to-student over several years. They form the primordial ocean of applications from which community and ISV codes emerge. Government research grants pay for the development of most personal codes. This paper reports on the nature and performance of this class of codes. Over the last year, I have looked at over two dozen personal codes from more than a dozen research institutions. The codes cover a variety of scientific fields, including astronomy, atmospheric sciences, bioinformatics, biology, chemistry, geology, and physics. The sources range from a few hundred lines to more than ten thousand lines, and are written in Fortran, Fortran 90, C, and C++. For the most part, the codes are modular, documented, and written in a clear, straightforward manner. They do not use complex language features, advanced data structures, programming tricks, or libraries. I had little trouble understanding what the codes did or how data structures were used. Most came with a makefile. Surprisingly, only one of the applications is parallel. All developers have access to parallel machines, so availability is not an issue. Several tried to parallelize their applications, but stopped after encountering difficulties. Lack of education and a perception that parallelism is difficult prevented most from trying. I parallelized several of the codes using OpenMP, and did not judge any of the codes as difficult to parallelize. Even more surprising than the lack of parallelism is the inefficiency of the codes. I was able to get large improvements in performance in a matter of a few days applying simple optimization techniques. Table 1 lists ten representative codes [names and affiliation are omitted to preserve anonymity]. Improvements on one processor range from 2x to 15.5x with a simple average of 4.75x. I did not use sophisticated performance tools or drill deep into the program's execution character as one would do when tuning ISV or community codes. Using only a profiler and source line timers, I identified inefficient sections of code and improved their performance by inspection. The changes were at a high level. I am sure there is another factor of 2 or 3 in each code, and more if the codes are parallelized. The study’s results show that personal scientific codes are running many times slower than they should and that the problem is pervasive. Computational scientists are not sloppy programmers; however, few are trained in the art of computer programming or code optimization. I found that most have a working knowledge of some programming language and standard software engineering practices; but they do not know, or think about, how to make their programs run faster. They simply do not know the standard techniques used to make codes run faster. In fact, they do not even perceive that such techniques exist. The case studies described in this paper show that applying simple, well known techniques can significantly increase the performance of personal codes. It is important that the scientific community and the Government agencies that support scientific research find ways to better educate academic scientific programmers. The inefficiency of their codes is so bad that it is retarding both the quality and progress of scientific research. # cacheperformance redundantoperations loopstructures performanceimprovement 1 x x 15.5 2 x 2.8 3 x x 2.5 4 x 2.1 5 x x 2.0 6 x 5.0 7 x 5.8 8 x 6.3 9 2.2 10 x x 3.3 Table 1 — Area of improvement and performance gains of 10 codes The remainder of the paper is organized as follows: sections 2, 3, and 4 discuss the three most common sources of inefficiencies in the codes studied. These are cache performance, redundant operations, and loop structures. Each section includes several examples. The last section summaries the work and suggests a possible solution to the issues raised. Optimizing cache performance Commodity microprocessor systems use caches to increase memory bandwidth and reduce memory latencies. Typical latencies from processor to L1, L2, local, and remote memory are 3, 10, 50, and 200 cycles, respectively. Moreover, bandwidth falls off dramatically as memory distances increase. Programs that do not use cache effectively run many times slower than programs that do. When optimizing for cache, the biggest performance gains are achieved by accessing data in cache order and reusing data to amortize the overhead of cache misses. Secondary considerations are prefetching, associativity, and replacement; however, the understanding and analysis required to optimize for the latter are probably beyond the capabilities of the non-expert. Much can be gained simply by accessing data in the correct order and maximizing data reuse. 6 out of the 10 codes studied here benefited from such high level optimizations. Array Accesses The most important cache optimization is the most basic: accessing Fortran array elements in column order and C array elements in row order. Four of the ten codes—1, 2, 4, and 10—got it wrong. Compilers will restructure nested loops to optimize cache performance, but may not do so if the loop structure is too complex, or the loop body includes conditionals, complex addressing, or function calls. In code 1, the compiler failed to invert a key loop because of complex addressing do I = 0, 1010, delta_x IM = I - delta_x IP = I + delta_x do J = 5, 995, delta_x JM = J - delta_x JP = J + delta_x T1 = CA1(IP, J) + CA1(I, JP) T2 = CA1(IM, J) + CA1(I, JM) S1 = T1 + T2 - 4 * CA1(I, J) CA(I, J) = CA1(I, J) + D * S1 end do end do In code 2, the culprit is conditionals do I = 1, N do J = 1, N If (IFLAG(I,J) .EQ. 0) then T1 = Value(I, J-1) T2 = Value(I-1, J) T3 = Value(I, J) T4 = Value(I+1, J) T5 = Value(I, J+1) Value(I,J) = 0.25 * (T1 + T2 + T5 + T4) Delta = ABS(T3 - Value(I,J)) If (Delta .GT. MaxDelta) MaxDelta = Delta endif enddo enddo I fixed both programs by inverting the loops by hand. Code 10 has three-dimensional arrays and triply nested loops. The structure of the most computationally intensive loops is too complex to invert automatically or by hand. The only practical solution is to transpose the arrays so that the dimension accessed by the innermost loop is in cache order. The arrays can be transposed at construction or prior to entering a computationally intensive section of code. The former requires all array references to be modified, while the latter is cost effective only if the cost of the transpose is amortized over many accesses. I used the second approach to optimize code 10. Code 5 has four-dimensional arrays and loops are nested four deep. For all of the reasons cited above the compiler is not able to restructure three key loops. Assume C arrays and let the four dimensions of the arrays be i, j, k, and l. In the original code, the index structure of the three loops is L1: for i L2: for i L3: for i for l for l for j for k for j for k for j for k for l So only L3 accesses array elements in cache order. L1 is a very complex loop—much too complex to invert. I brought the loop into cache alignment by transposing the second and fourth dimensions of the arrays. Since the code uses a macro to compute all array indexes, I effected the transpose at construction and changed the macro appropriately. The dimensions of the new arrays are now: i, l, k, and j. L3 is a simple loop and easily inverted. L2 has a loop-carried scalar dependence in k. By promoting the scalar name that carries the dependence to an array, I was able to invert the third and fourth subloops aligning the loop with cache. Code 5 is by far the most difficult of the four codes to optimize for array accesses; but the knowledge required to fix the problems is no more than that required for the other codes. I would judge this code at the limits of, but not beyond, the capabilities of appropriately trained computational scientists. Array Strides When a cache miss occurs, a line (64 bytes) rather than just one word is loaded into the cache. If data is accessed stride 1, than the cost of the miss is amortized over 8 words. Any stride other than one reduces the cost savings. Two of the ten codes studied suffered from non-unit strides. The codes represent two important classes of "strided" codes. Code 1 employs a multi-grid algorithm to reduce time to convergence. The grids are every tenth, fifth, second, and unit element. Since time to convergence is inversely proportional to the distance between elements, coarse grids converge quickly providing good starting values for finer grids. The better starting values further reduce the time to convergence. The downside is that grids of every nth element, n > 1, introduce non-unit strides into the computation. In the original code, much of the savings of the multi-grid algorithm were lost due to this problem. I eliminated the problem by compressing (copying) coarse grids into continuous memory, and rewriting the computation as a function of the compressed grid. On convergence, I copied the final values of the compressed grid back to the original grid. The savings gained from unit stride access of the compressed grid more than paid for the cost of copying. Using compressed grids, the loop from code 1 included in the previous section becomes do j = 1, GZ do i = 1, GZ T1 = CA(i+0, j-1) + CA(i-1, j+0) T4 = CA1(i+1, j+0) + CA1(i+0, j+1) S1 = T1 + T4 - 4 * CA1(i+0, j+0) CA(i+0, j+0) = CA1(i+0, j+0) + DD * S1 enddo enddo where CA and CA1 are compressed arrays of size GZ. Code 7 traverses a list of objects selecting objects for later processing. The labels of the selected objects are stored in an array. The selection step has unit stride, but the processing steps have irregular stride. A fix is to save the parameters of the selected objects in temporary arrays as they are selected, and pass the temporary arrays to the processing functions. The fix is practical if the same parameters are used in selection as in processing, or if processing comprises a series of distinct steps which use overlapping subsets of the parameters. Both conditions are true for code 7, so I achieved significant improvement by copying parameters to temporary arrays during selection. Data reuse In the previous sections, we optimized for spatial locality. It is also important to optimize for temporal locality. Once read, a datum should be used as much as possible before it is forced from cache. Loop fusion and loop unrolling are two techniques that increase temporal locality. Unfortunately, both techniques increase register pressure—as loop bodies become larger, the number of registers required to hold temporary values grows. Once register spilling occurs, any gains evaporate quickly. For multiprocessors with small register sets or small caches, the sweet spot can be very small. In the ten codes presented here, I found no opportunities for loop fusion and only two opportunities for loop unrolling (codes 1 and 3). In code 1, unrolling the outer and inner loop one iteration increases the number of result values computed by the loop body from 1 to 4, do J = 1, GZ-2, 2 do I = 1, GZ-2, 2 T1 = CA1(i+0, j-1) + CA1(i-1, j+0) T2 = CA1(i+1, j-1) + CA1(i+0, j+0) T3 = CA1(i+0, j+0) + CA1(i-1, j+1) T4 = CA1(i+1, j+0) + CA1(i+0, j+1) T5 = CA1(i+2, j+0) + CA1(i+1, j+1) T6 = CA1(i+1, j+1) + CA1(i+0, j+2) T7 = CA1(i+2, j+1) + CA1(i+1, j+2) S1 = T1 + T4 - 4 * CA1(i+0, j+0) S2 = T2 + T5 - 4 * CA1(i+1, j+0) S3 = T3 + T6 - 4 * CA1(i+0, j+1) S4 = T4 + T7 - 4 * CA1(i+1, j+1) CA(i+0, j+0) = CA1(i+0, j+0) + DD * S1 CA(i+1, j+0) = CA1(i+1, j+0) + DD * S2 CA(i+0, j+1) = CA1(i+0, j+1) + DD * S3 CA(i+1, j+1) = CA1(i+1, j+1) + DD * S4 enddo enddo The loop body executes 12 reads, whereas as the rolled loop shown in the previous section executes 20 reads to compute the same four values. In code 3, two loops are unrolled 8 times and one loop is unrolled 4 times. Here is the before for (k = 0; k < NK[u]; k++) { sum = 0.0; for (y = 0; y < NY; y++) { sum += W[y][u][k] * delta[y]; } backprop[i++]=sum; } and after code for (k = 0; k < KK - 8; k+=8) { sum0 = 0.0; sum1 = 0.0; sum2 = 0.0; sum3 = 0.0; sum4 = 0.0; sum5 = 0.0; sum6 = 0.0; sum7 = 0.0; for (y = 0; y < NY; y++) { sum0 += W[y][0][k+0] * delta[y]; sum1 += W[y][0][k+1] * delta[y]; sum2 += W[y][0][k+2] * delta[y]; sum3 += W[y][0][k+3] * delta[y]; sum4 += W[y][0][k+4] * delta[y]; sum5 += W[y][0][k+5] * delta[y]; sum6 += W[y][0][k+6] * delta[y]; sum7 += W[y][0][k+7] * delta[y]; } backprop[k+0] = sum0; backprop[k+1] = sum1; backprop[k+2] = sum2; backprop[k+3] = sum3; backprop[k+4] = sum4; backprop[k+5] = sum5; backprop[k+6] = sum6; backprop[k+7] = sum7; } for one of the loops unrolled 8 times. Optimizing for temporal locality is the most difficult optimization considered in this paper. The concepts are not difficult, but the sweet spot is small. Identifying where the program can benefit from loop unrolling or loop fusion is not trivial. Moreover, it takes some effort to get it right. Still, educating scientific programmers about temporal locality and teaching them how to optimize for it will pay dividends. Reducing instruction count Execution time is a function of instruction count. Reduce the count and you usually reduce the time. The best solution is to use a more efficient algorithm; that is, an algorithm whose order of complexity is smaller, that converges quicker, or is more accurate. Optimizing source code without changing the algorithm yields smaller, but still significant, gains. This paper considers only the latter because the intent is to study how much better codes can run if written by programmers schooled in basic code optimization techniques. The ten codes studied benefited from three types of "instruction reducing" optimizations. The two most prevalent were hoisting invariant memory and data operations out of inner loops. The third was eliminating unnecessary data copying. The nature of these inefficiencies is language dependent. Memory operations The semantics of C make it difficult for the compiler to determine all the invariant memory operations in a loop. The problem is particularly acute for loops in functions since the compiler may not know the values of the function's parameters at every call site when compiling the function. Most compilers support pragmas to help resolve ambiguities; however, these pragmas are not comprehensive and there is no standard syntax. To guarantee that invariant memory operations are not executed repetitively, the user has little choice but to hoist the operations by hand. The problem is not as severe in Fortran programs because in the absence of equivalence statements, it is a violation of the language's semantics for two names to share memory. Codes 3 and 5 are C programs. In both cases, the compiler did not hoist all invariant memory operations from inner loops. Consider the following loop from code 3 for (y = 0; y < NY; y++) { i = 0; for (u = 0; u < NU; u++) { for (k = 0; k < NK[u]; k++) { dW[y][u][k] += delta[y] * I1[i++]; } } } Since dW[y][u] can point to the same memory space as delta for one or more values of y and u, assignment to dW[y][u][k] may change the value of delta[y]. In reality, dW and delta do not overlap in memory, so I rewrote the loop as for (y = 0; y < NY; y++) { i = 0; Dy = delta[y]; for (u = 0; u < NU; u++) { for (k = 0; k < NK[u]; k++) { dW[y][u][k] += Dy * I1[i++]; } } } Failure to hoist invariant memory operations may be due to complex address calculations. If the compiler can not determine that the address calculation is invariant, then it can hoist neither the calculation nor the associated memory operations. As noted above, code 5 uses a macro to address four-dimensional arrays #define MAT4D(a,q,i,j,k) (double *)((a)->data + (q)*(a)->strides[0] + (i)*(a)->strides[3] + (j)*(a)->strides[2] + (k)*(a)->strides[1]) The macro is too complex for the compiler to understand and so, it does not identify any subexpressions as loop invariant. The simplest way to eliminate the address calculation from the innermost loop (over i) is to define a0 = MAT4D(a,q,0,j,k) before the loop and then replace all instances of *MAT4D(a,q,i,j,k) in the loop with a0[i] A similar problem appears in code 6, a Fortran program. The key loop in this program is do n1 = 1, nh nx1 = (n1 - 1) / nz + 1 nz1 = n1 - nz * (nx1 - 1) do n2 = 1, nh nx2 = (n2 - 1) / nz + 1 nz2 = n2 - nz * (nx2 - 1) ndx = nx2 - nx1 ndy = nz2 - nz1 gxx = grn(1,ndx,ndy) gyy = grn(2,ndx,ndy) gxy = grn(3,ndx,ndy) balance(n1,1) = balance(n1,1) + (force(n2,1) * gxx + force(n2,2) * gxy) * h1 balance(n1,2) = balance(n1,2) + (force(n2,1) * gxy + force(n2,2) * gyy)*h1 end do end do The programmer has written this loop well—there are no loop invariant operations with respect to n1 and n2. However, the loop resides within an iterative loop over time and the index calculations are independent with respect to time. Trading space for time, I precomputed the index values prior to the entering the time loop and stored the values in two arrays. I then replaced the index calculations with reads of the arrays. Data operations Ways to reduce data operations can appear in many forms. Implementing a more efficient algorithm produces the biggest gains. The closest I came to an algorithm change was in code 4. This code computes the inner product of K-vectors A(i) and B(j), 0 = i < N, 0 = j < M, for most values of i and j. Since the program computes most of the NM possible inner products, it is more efficient to compute all the inner products in one triply-nested loop rather than one at a time when needed. The savings accrue from reading A(i) once for all B(j) vectors and from loop unrolling. for (i = 0; i < N; i+=8) { for (j = 0; j < M; j++) { sum0 = 0.0; sum1 = 0.0; sum2 = 0.0; sum3 = 0.0; sum4 = 0.0; sum5 = 0.0; sum6 = 0.0; sum7 = 0.0; for (k = 0; k < K; k++) { sum0 += A[i+0][k] * B[j][k]; sum1 += A[i+1][k] * B[j][k]; sum2 += A[i+2][k] * B[j][k]; sum3 += A[i+3][k] * B[j][k]; sum4 += A[i+4][k] * B[j][k]; sum5 += A[i+5][k] * B[j][k]; sum6 += A[i+6][k] * B[j][k]; sum7 += A[i+7][k] * B[j][k]; } C[i+0][j] = sum0; C[i+1][j] = sum1; C[i+2][j] = sum2; C[i+3][j] = sum3; C[i+4][j] = sum4; C[i+5][j] = sum5; C[i+6][j] = sum6; C[i+7][j] = sum7; }} This change requires knowledge of a typical run; i.e., that most inner products are computed. The reasons for the change, however, derive from basic optimization concepts. It is the type of change easily made at development time by a knowledgeable programmer. In code 5, we have the data version of the index optimization in code 6. Here a very expensive computation is a function of the loop indices and so cannot be hoisted out of the loop; however, the computation is invariant with respect to an outer iterative loop over time. We can compute its value for each iteration of the computation loop prior to entering the time loop and save the values in an array. The increase in memory required to store the values is small in comparison to the large savings in time. The main loop in Code 8 is doubly nested. The inner loop includes a series of guarded computations; some are a function of the inner loop index but not the outer loop index while others are a function of the outer loop index but not the inner loop index for (j = 0; j < N; j++) { for (i = 0; i < M; i++) { r = i * hrmax; R = A[j]; temp = (PRM[3] == 0.0) ? 1.0 : pow(r, PRM[3]); high = temp * kcoeff * B[j] * PRM[2] * PRM[4]; low = high * PRM[6] * PRM[6] / (1.0 + pow(PRM[4] * PRM[6], 2.0)); kap = (R > PRM[6]) ? high * R * R / (1.0 + pow(PRM[4]*r, 2.0) : low * pow(R/PRM[6], PRM[5]); < rest of loop omitted > }} Note that the value of temp is invariant to j. Thus, we can hoist the computation for temp out of the loop and save its values in an array. for (i = 0; i < M; i++) { r = i * hrmax; TEMP[i] = pow(r, PRM[3]); } [N.B. – the case for PRM[3] = 0 is omitted and will be reintroduced later.] We now hoist out of the inner loop the computations invariant to i. Since the conditional guarding the value of kap is invariant to i, it behooves us to hoist the computation out of the inner loop, thereby executing the guard once rather than M times. The final version of the code is for (j = 0; j < N; j++) { R = rig[j] / 1000.; tmp1 = kcoeff * par[2] * beta[j] * par[4]; tmp2 = 1.0 + (par[4] * par[4] * par[6] * par[6]); tmp3 = 1.0 + (par[4] * par[4] * R * R); tmp4 = par[6] * par[6] / tmp2; tmp5 = R * R / tmp3; tmp6 = pow(R / par[6], par[5]); if ((par[3] == 0.0) && (R > par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * tmp5; } else if ((par[3] == 0.0) && (R <= par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * tmp4 * tmp6; } else if ((par[3] != 0.0) && (R > par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * TEMP[i] * tmp5; } else if ((par[3] != 0.0) && (R <= par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * TEMP[i] * tmp4 * tmp6; } for (i = 0; i < M; i++) { kap = KAP[i]; r = i * hrmax; < rest of loop omitted > } } Maybe not the prettiest piece of code, but certainly much more efficient than the original loop, Copy operations Several programs unnecessarily copy data from one data structure to another. This problem occurs in both Fortran and C programs, although it manifests itself differently in the two languages. Code 1 declares two arrays—one for old values and one for new values. At the end of each iteration, the array of new values is copied to the array of old values to reset the data structures for the next iteration. This problem occurs in Fortran programs not included in this study and in both Fortran 77 and Fortran 90 code. Introducing pointers to the arrays and swapping pointer values is an obvious way to eliminate the copying; but pointers is not a feature that many Fortran programmers know well or are comfortable using. An easy solution not involving pointers is to extend the dimension of the value array by 1 and use the last dimension to differentiate between arrays at different times. For example, if the data space is N x N, declare the array (N, N, 2). Then store the problem’s initial values in (_, _, 2) and define the scalar names new = 2 and old = 1. At the start of each iteration, swap old and new to reset the arrays. The old–new copy problem did not appear in any C program. In programs that had new and old values, the code swapped pointers to reset data structures. Where unnecessary coping did occur is in structure assignment and parameter passing. Structures in C are handled much like scalars. Assignment causes the data space of the right-hand name to be copied to the data space of the left-hand name. Similarly, when a structure is passed to a function, the data space of the actual parameter is copied to the data space of the formal parameter. If the structure is large and the assignment or function call is in an inner loop, then copying costs can grow quite large. While none of the ten programs considered here manifested this problem, it did occur in programs not included in the study. A simple fix is always to refer to structures via pointers. Optimizing loop structures Since scientific programs spend almost all their time in loops, efficient loops are the key to good performance. Conditionals, function calls, little instruction level parallelism, and large numbers of temporary values make it difficult for the compiler to generate tightly packed, highly efficient code. Conditionals and function calls introduce jumps that disrupt code flow. Users should eliminate or isolate conditionls to their own loops as much as possible. Often logical expressions can be substituted for if-then-else statements. For example, code 2 includes the following snippet MaxDelta = 0.0 do J = 1, N do I = 1, M < code omitted > Delta = abs(OldValue ? NewValue) if (Delta > MaxDelta) MaxDelta = Delta enddo enddo if (MaxDelta .gt. 0.001) goto 200 Since the only use of MaxDelta is to control the jump to 200 and all that matters is whether or not it is greater than 0.001, I made MaxDelta a boolean and rewrote the snippet as MaxDelta = .false. do J = 1, N do I = 1, M < code omitted > Delta = abs(OldValue ? NewValue) MaxDelta = MaxDelta .or. (Delta .gt. 0.001) enddo enddo if (MaxDelta) goto 200 thereby, eliminating the conditional expression from the inner loop. A microprocessor can execute many instructions per instruction cycle. Typically, it can execute one or more memory, floating point, integer, and jump operations. To be executed simultaneously, the operations must be independent. Thick loops tend to have more instruction level parallelism than thin loops. Moreover, they reduce memory traffice by maximizing data reuse. Loop unrolling and loop fusion are two techniques to increase the size of loop bodies. Several of the codes studied benefitted from loop unrolling, but none benefitted from loop fusion. This observation is not too surpising since it is the general tendency of programmers to write thick loops. As loops become thicker, the number of temporary values grows, increasing register pressure. If registers spill, then memory traffic increases and code flow is disrupted. A thick loop with many temporary values may execute slower than an equivalent series of thin loops. The biggest gain will be achieved if the thick loop can be split into a series of independent loops eliminating the need to write and read temporary arrays. I found such an occasion in code 10 where I split the loop do i = 1, n do j = 1, m A24(j,i)= S24(j,i) * T24(j,i) + S25(j,i) * U25(j,i) B24(j,i)= S24(j,i) * T25(j,i) + S25(j,i) * U24(j,i) A25(j,i)= S24(j,i) * C24(j,i) + S25(j,i) * V24(j,i) B25(j,i)= S24(j,i) * U25(j,i) + S25(j,i) * V25(j,i) C24(j,i)= S26(j,i) * T26(j,i) + S27(j,i) * U26(j,i) D24(j,i)= S26(j,i) * T27(j,i) + S27(j,i) * V26(j,i) C25(j,i)= S27(j,i) * S28(j,i) + S26(j,i) * U28(j,i) D25(j,i)= S27(j,i) * T28(j,i) + S26(j,i) * V28(j,i) end do end do into two disjoint loops do i = 1, n do j = 1, m A24(j,i)= S24(j,i) * T24(j,i) + S25(j,i) * U25(j,i) B24(j,i)= S24(j,i) * T25(j,i) + S25(j,i) * U24(j,i) A25(j,i)= S24(j,i) * C24(j,i) + S25(j,i) * V24(j,i) B25(j,i)= S24(j,i) * U25(j,i) + S25(j,i) * V25(j,i) end do end do do i = 1, n do j = 1, m C24(j,i)= S26(j,i) * T26(j,i) + S27(j,i) * U26(j,i) D24(j,i)= S26(j,i) * T27(j,i) + S27(j,i) * V26(j,i) C25(j,i)= S27(j,i) * S28(j,i) + S26(j,i) * U28(j,i) D25(j,i)= S27(j,i) * T28(j,i) + S26(j,i) * V28(j,i) end do end do Conclusions Over the course of the last year, I have had the opportunity to work with over two dozen academic scientific programmers at leading research universities. Their research interests span a broad range of scientific fields. Except for two programs that relied almost exclusively on library routines (matrix multiply and fast Fourier transform), I was able to improve significantly the single processor performance of all codes. Improvements range from 2x to 15.5x with a simple average of 4.75x. Changes to the source code were at a very high level. I did not use sophisticated techniques or programming tools to discover inefficiencies or effect the changes. Only one code was parallel despite the availability of parallel systems to all developers. Clearly, we have a problem—personal scientific research codes are highly inefficient and not running parallel. The developers are unaware of simple optimization techniques to make programs run faster. They lack education in the art of code optimization and parallel programming. I do not believe we can fix the problem by publishing additional books or training manuals. To date, the developers in questions have not studied the books or manual available, and are unlikely to do so in the future. Short courses are a possible solution, but I believe they are too concentrated to be much use. The general concepts can be taught in a three or four day course, but that is not enough time for students to practice what they learn and acquire the experience to apply and extend the concepts to their codes. Practice is the key to becoming proficient at optimization. I recommend that graduate students be required to take a semester length course in optimization and parallel programming. We would never give someone access to state-of-the-art scientific equipment costing hundreds of thousands of dollars without first requiring them to demonstrate that they know how to use the equipment. Yet the criterion for time on state-of-the-art supercomputers is at most an interesting project. Requestors are never asked to demonstrate that they know how to use the system, or can use the system effectively. A semester course would teach them the required skills. Government agencies that fund academic scientific research pay for most of the computer systems supporting scientific research as well as the development of most personal scientific codes. These agencies should require graduate schools to offer a course in optimization and parallel programming as a requirement for funding. About the Author John Feo received his Ph.D. in Computer Science from The University of Texas at Austin in 1986. After graduate school, Dr. Feo worked at Lawrence Livermore National Laboratory where he was the Group Leader of the Computer Research Group and principal investigator of the Sisal Language Project. In 1997, Dr. Feo joined Tera Computer Company where he was project manager for the MTA, and oversaw the programming and evaluation of the MTA at the San Diego Supercomputer Center. In 2000, Dr. Feo joined Sun Microsystems as an HPC application specialist. He works with university research groups to optimize and parallelize scientific codes. Dr. Feo has published over two dozen research articles in the areas of parallel parallel programming, parallel programming languages, and application performance.

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Flash IO error while uploading photo with low uploading internet speed

- by Beck

Actionscript: System.security.allowDomain("http://" + _root.tdomain + "/"); import flash.net.FileReferenceList; import flash.net.FileReference; import flash.external.ExternalInterface; import flash.external.*; /* Main variables */ var session_photos = _root.ph; var how_much_you_can_upload = 0; var selected_photos; // container for selected photos var inside_photo_num = 0; // for photo in_array selection var created_elements = _root.ph; var for_js_num = _root.ph; /* Functions & settings for javascript<->flash conversation */ var methodName:String = "addtoflash"; var instance:Object = null; var method:Function = addnewphotonumber; var wasSuccessful:Boolean = ExternalInterface.addCallback(methodName, instance, method); function addnewphotonumber() { session_photos--; created_elements--; for_js_num--; } /* Javascript hide and show flash button functions */ function block(){getURL("Javascript: blocking();");} function unblock(){getURL("Javascript:unblocking();");} /* Creating HTML platform function */ var result = false; /* Uploading */ function uploadthis(photos:Array) { if(!photos[inside_photo_num].upload("http://" + _root.tdomain + "/upload.php?PHPSESSID=" + _root.phpsessionid)) { getURL("Javascript:error_uploading();"); } } /* Flash button(applet) options and bindings */ var fileTypes:Array = new Array(); var imageTypes:Object = new Object(); imageTypes.description = "Images (*.jpg)"; imageTypes.extension = "*.jpg;"; fileTypes.push(imageTypes); var fileListener:Object = new Object(); var btnListener:Object = new Object(); btnListener.click = function(eventObj:Object) { var fileRef:FileReferenceList = new FileReferenceList(); fileRef.addListener(fileListener); fileRef.browse(fileTypes); } uploadButton.addEventListener("click", btnListener); /* Listeners */ fileListener.onSelect = function(fileRefList:FileReferenceList):Void { // reseting values inside_photo_num = 0; var list:Array = fileRefList.fileList; var item:FileReference; // PHP photo counter how_much_you_can_upload = 3 - session_photos; if(list.length > how_much_you_can_upload) { getURL("Javascript:howmuch=" + how_much_you_can_upload + ";list_length=" + list.length + ";limit_reached();"); return; } // if session variable isn't yet refreshed, we check inner counter if(created_elements >= 3) { getURL("Javascript:limit_reached();"); return; } selected_photos = list; for(var i:Number = 0; i < list.length; i++) { how_much_you_can_upload--; item = list[i]; trace("name: " + item.name); trace(item.addListener(this)); if((item.size / 1024) > 5000) {getURL("Javascript:size_limit_reached();");return;} } result = false; setTimeout(block,500); /* Increment number for new HTML container and pass it to javascript, after javascript returns true and we start uploading */ for_js_num++; if(ExternalInterface.call("create_platform",for_js_num)) { uploadthis(selected_photos); } } fileListener.onProgress = function(file:FileReference, bytesLoaded:Number, bytesTotal:Number):Void { getURL("Javascript:files_process(" + bytesLoaded + "," + bytesTotal + "," + for_js_num + ");"); } fileListener.onComplete = function(file:FileReference, bytesLoaded:Number, bytesTotal:Number):Void { inside_photo_num++; var sendvar_lv:LoadVars = new LoadVars(); var loadvar_lv:LoadVars = new LoadVars(); loadvar_lv.onLoad = function(success:Boolean){ if(loadvar_lv.failed == 1) { getURL("Javascript:type_failed();"); return; } getURL("Javascript:filelinks='" + loadvar_lv.json + "';fullname='" + loadvar_lv.fullname + "';completed(" + for_js_num + ");"); created_elements++; if((inside_photo_num + 1) > selected_photos.length) {setTimeout(unblock,1000);return;} // don't create empty containers anymore if(created_elements >= 3) {return;} result = false; /* Increment number for new HTML container and pass it to javascript, after javascript returns true and we start uploading */ for_js_num++; if(ExternalInterface.call("create_platform",for_js_num)) { uploadthis(selected_photos); } } sendvar_lv.getnum = true; sendvar_lv.PHPSESSID = _root.phpsessionid; sendvar_lv.sendAndLoad("http://" + _root.tdomain + "/upload.php",loadvar_lv,"POST"); } fileListener.onCancel = function(file:FileReference):Void { } fileListener.onOpen = function(file:FileReference):Void { } fileListener.onHTTPError = function(file:FileReference, httpError:Number):Void { getURL("Javascript:http_error(" + httpError + ");"); } fileListener.onSecurityError = function(file:FileReference, errorString:String):Void { getURL("Javascript:security_error(" + errorString + ");"); } fileListener.onIOError = function(file:FileReference):Void { getURL("Javascript:io_error();"); selected_photos[inside_photo_num].cancel(); uploadthis(selected_photos); } <PARAM name="allowScriptAccess" value="always"> <PARAM name="swliveconnect" value="true"> <PARAM name="movie" value="http://www.localh.com/fileref.swf?ph=0&phpsessionid=8mirsjsd75v6vk583vkus50qbb2djsp6&tdomain=www.localh.com"> <PARAM name="wmode" value="opaque"> <PARAM name="quality" value="high"> <PARAM name="bgcolor" value="#ffffff"> <EMBED swliveconnect="true" wmode="opaque" src="http://www.localh.com/fileref.swf?ph=0&phpsessionid=8mirsjsd75v6vk583vkus50qbb2djsp6&tdomain=www.localh.com" quality="high" bgcolor="#ffffff" width="100" height="22" name="fileref" align="middle" allowScriptAccess="always" type="application/x-shockwave-flash" pluginspage="http://www.macromedia.com/go/getflashplayer"></EMBED> My uploading speed is 40kb/sec Getting flash error while uploading photos bigger than 500kb and getting no error while uploading photos less than 100-500kb~. My friend has 8mbit uploading speed and has no errors even while uploading 3.2mb photos and more. How to fix this problem? I have tried to re-upload on IO error trigger, but it stops at the same place. Any solution regarding this error? By the way, i was watching process via debugging proxy and figured out, that responce headers doesn't come at all on this IO error. And sometimes shows socket error. If need, i will post serverside php script as well. But it stops at if(isset($_FILES['Filedata'])) { so it won't help :) as all processing comes after this check.

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How to Submit Form Given Specific Json Response

- by dentalhero

I'm new to Json, so please excuse the newb question. I have a form in which I'm conducting an Ajax post to submit address information to a backend script for validation. Here's the form: <form name="Form" id="Forms" method="post" action="WebCatPageServer.exe" class="uniForm"> <input name="Action" type="hidden" value="SHIPTOVALIDATE"/> <input name="IsAjax" type="hidden" value="Yes"/>  <fieldset class="inlineLabels top"> <h2>Order Details</h2> <div class="ctrlHolder first"> <label for="orderdesc">Order Description</label> <input name="Order Desc" id="OrderDesc" type="text" class="textInput small" tabindex="1" value=""/> </div> <div class="ctrlHolder"> <label for="po">PO # <span class="redasterisk">*</span></label> <input name="Cust Po" id="PoJobNo" type="text" class="textInput small required" maxlength="20" tabindex="2" value="dgnfg"/> </div>  <div class="ctrlHolder"> <label for="shipvia">Ship Via <span class="redasterisk">*</span></label> <select name="Ship Via" id="shipvia" class="selectInput small required" tabindex="4"/> <option value="" class="default">Select Ship Method</option> <option value="OT - Our Truck" class="del" selected>Our Truck</option> <option value="WC - Will Call" class="pick">Will Call</option> </select> </div> <div class="ctrlHolder" id="pickupdate"> <label for="datepickup">Requested Pickup Date <span class="redasterisk">*</span></label> <input name="datepickup" id="datepickup" type="text" class="textInput small" tabindex="5" value="11/09/2012"> </div> <div class="ctrlHolder" id="shipdate"> <label for="dateship">Requested Delivery Date <span class="redasterisk">*</span></label> <input name="dateship" id="dateship" type="text" class="textInput small" value="" tabindex="6"> </div> <div class="ctrlHolder" id="shipto"> <label for="ShipTo">Ship To <span class="redasterisk">*</span></label> <select name="ShipTos" id="ShipTos" class="selectInput auto required" tabindex="7"> <option value="">Select an Option</option> <option value="ShipToManual" class="manual">Manually Enter Address</option> <option value="0">A ACTION AIR*, 5241 YANCEYVILLE, COLUMBIA, SC 29214-0001</option> <option value="1">A ACTION AIR*, 649 spring lane, sanford, NC 27330</option> <option value="2">A ACTION AIR*, 1313 south briggs avenue, durham, NC 27703</option> <option value="3">A ACTION AIR*, 112 cricket hill lane, cary, NC 27513</option> <option value="4">A ACTION AIR*, 2911 duke homestead road, durham, NC 27705</option> <option value="5">A ACTION AIR*, chickem poop, atlanta, GA 60609</option> </select> <br /> </div> </fieldset> <fieldset class="inlineLabels" id="shipinfo"> <h2>Shipping Information</h2> <div class="ctrlHolder first"> <label for="YourName">Your Name <span class="redasterisk">*</span></label> <input name="Your Name" id="Your_Name" type="text" class="textInput small required" tabindex="8" value="" /> </div> <div class="ctrlHolder"> <label for="CompanyName">Company Name <span class="redasterisk">*</span></label> <input name="Company Name" id="CompanyName" type="text" class="textInput small required" tabindex="9" value="A ACTION AIR*"/> </div> <div class="ctrlHolder"> <label for="Address1">Address 1 <span class="redasterisk">*</span></label> <input name="Address_1" id="Address_1" type="text" maxlength="30" class="textInput small required" tabindex="10" value="5241 YANCEYVILLE"/> </div> <div class="ctrlHolder"> <label for="Address2">Address 2</label> <input name="Address_2" id="Address_2" type="text" maxlength="30" class="textInput small" tabindex="11" value=""/> </div> <div class="ctrlHolder"> <label for="City">City <span class="redasterisk">*</span></label> <input name="City" id="City" type="text" maxlength="25" class="textInput small required" tabindex="12" value="COLUMBIA"/> </div> <div class="ctrlHolder"> <label for="State">State <span class="redasterisk">*</span></label> <select name="State" id="State" class="selectInput small required" tabindex="13"> <option value="">Select State</option> <option value="AL">Alabama</option> <option value="AK">Alaska</option> <option value="AZ">Arizona</option> <option value="AR">Arkansas</option> <option value="CA">California</option> <option value="CO">Colorado</option> <option value="CT">Connecticut</option> <option value="DE">Delaware</option> <option value="FL">Florida</option> <option value="GA">Georgia</option> <option value="HI">Hawaii</option> <option value="ID">Idaho</option> <option value="IL">Illinois</option> <option value="IN">Indiana</option> <option value="IA">Iowa</option> <option value="KS">Kansas</option> <option value="KY">Kentucky</option> <option value="LA">Louisiana</option> <option value="ME">Maine</option> <option value="MD">Maryland</option> <option value="MA">Massachussetts</option> <option value="MI">Michigan</option> <option value="MN">Minnesota</option> <option value="MS">Mississippi</option> <option value="MO">Missouri</option> <option value="MT">Montana</option> <option value="NE">Nebraska</option> <option value="NV">Nevada</option> <option value="NH">New Hampshire</option> <option value="NJ">New Jersey</option> <option value="NM">New Mexico</option> <option value="NY">New York</option> <option value="NC">North Carolina</option> <option value="ND">North Dakota</option> <option value="OH">Ohio</option> <option value="OK">Oklahoma</option> <option value="OR">Oregon</option> <option value="PA">Pennsylvania</option> <option value="RI">Rhode Island</option> <option value="SC" selected>South Carolina</option> <option value="SD">South Dakota</option> <option value="TN">Tennessee</option> <option value="TX">Texas</option> <option value="UT">Utah</option> <option value="VT">Vermont</option> <option value="VA">Virginia</option> <option value="WA">Washington</option> <option value="WV">West Virginia</option> <option value="WI">Wisconsin</option> <option value="WY">Wyoming</option> </select> </div> <div class="ctrlHolder"> <label for="ZipCode">Zip Code <span class="redasterisk">*</span></label> <input name="Zip" id="Zip" type="text" maxlength="10" class="textInput small required zipcode" tabindex="14" value=""/> </div> <div class="ctrlHolder"> <label for="Phone">Phone <span class="redasterisk">*</span></label> <input name="Phone Number" id="Phone" type="text" class="textInput small required phone" alt="phone-us" tabindex="15" value="(336)954-5009"/> </div> <div class="ctrlHolder"> <label for="Fax">Fax</label> <input name="FaxNumber" id="Fax Number" type="text" class="textInput small fax" alt="phone-us" tabindex="16" value=""/> </div> <div class="ctrlHolder"> <label for="">E-mail <span class="redasterisk">*</span></label> <input name="Email" id="Email" type="text" class="textInput small required email" tabindex="17" value=""/> </div> </fieldset> <fieldset class="inlineLabels"> <h2>Order/Shipping Notes</h2> <div class="ctrlHolder first"> <label for="notes">Order Notes </label> <textarea name="OrderNotes" id="ta" cols="26" rows="7" tabindex="18"></textarea><br /> <p class="formHint"><b>(Maximum characters: 175)   <span id="charLeft"></span>   Characters left</b><br /> (Cross streets, special instructions, etc.)</p> <br /> </div> </fieldset> <fieldset class="inlineLabels"> <h2>Continue To Next Step</h2> <div class="buttonHolder"> <label for="freightmsg">**Applicable freight charges will be applied at the time of invoicing.**</label> <input name="continuetocheckout" type="submit" class="button red smallrounded" value="Continue >" alt="Continue to Next Step" tabindex="20"/> </div> </fieldset> </form> AJAX Call Here's the AJAX call: $(function() { $("#Forms").submit(function() { $.ajax({ type: 'post', url: 'WebCatPageServer.exe', dataType : 'json', data: $("#Forms").serialize(), complete:function(data){ alert(data); } }); return false; }); }); JSON Response Here's the JSON response: {"DidValidate":true,"Company Name":"A ACTION AIR*","AddrLine1":"5241 YANCEYVILLE","AddrLine2":"","City":"COLUMBIA","State":"SC","Zip":"","Modified":false,"AddressError":false,"ZipError":false} Question: How do I submit the form programatically if both AddressError and ZipError return with a false?

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Need some help on how to replay the last game of a java maze game

- by Marty

Hello, I am working on creating a Java maze game for a project. The maze is displayed on the console as standard output not in an applet. I have created most of hte code I need, however I am stuck at one problem and that is I need a user to be able to replay the last game i.e redraw the maze with the users moves but without any input from the user. I am not sure on what course of action to take, i was thinking about copying each users move or the position of each move into another array, as you can see i have 2 variables which hold the position of the player, plyrX and plyrY do you think copying these values into a new array after each move would solve my problem and how would i go about this? I have updated my code, apologies about the textIO.java class not being present, not sure how to resolve that exept post a link to TextIO.java [TextIO.java][1] My code below is updated with a new array of type char to hold values from the original maze (read in from text file and displayed using unicode characters) and also to new variables c_plyrX and c_plyrY which I am thinking should hold the values of plyrX and plyrY and copy them into the new array. When I try to call the replayGame(); method from the menu the maze loads for a second then the console exits so im not sure what I am doing wrong Thanks public class MazeGame { //unicode characters that will define the maze walls, //pathways, and in game characters. final static char WALL = '\u2588'; //wall final static char PATH = '\u2591'; //pathway final static char PLAYER = '\u25EF'; //player final static char ENTRANCE = 'E'; //entrance final static char EXIT = '\u2716'; //exit //declaring member variables which will hold the maze co-ordinates //X = rows, Y = columns static int entX = 0; //entrance X co-ordinate static int entY = 1; //entrance y co-ordinate static int plyrX = 0; static int plyrY = 1; static int exitX = 24; //exit X co-ordinate static int exitY = 37; //exit Y co-ordinate //static member variables which hold maze values //used so values can be accessed from different methods static int rows; //rows variable static int cols; //columns variable static char[][] maze; //defines 2 dimensional array to hold the maze //variables that hold player movement values static char dir; //direction static int spaces; //amount of spaces user can travel //variable to hold amount of moves the user has taken; static int movesTaken = 0; //new array to hold player moves for replaying game static char[][] mazeCopy; static int c_plyrX; static int c_plyrY; /** userMenu method for displaying the user menu which will provide various options for * the user to choose such as play a maze game, get instructions, etc. */ public static void userMenu(){ TextIO.putln("Maze Game"); TextIO.putln("*********"); TextIO.putln("Choose an option."); TextIO.putln(""); TextIO.putln("1. Play the Maze Game."); TextIO.putln("2. View Instructions."); TextIO.putln("3. Replay the last game."); TextIO.putln("4. Exit the Maze Game."); TextIO.putln(""); int option; //variable for holding users option TextIO.put("Type your choice: "); option = TextIO.getlnInt(); //gets users option //switch statement for processing menu options switch(option){ case 1: playMazeGame(); case 2: instructions(); case 3: if (c_plyrX == plyrX && c_plyrY == plyrY)replayGame(); else { TextIO.putln("Option not available yet, you need to play a game first."); TextIO.putln(); userMenu(); } case 4: System.exit(0); //exits the user out of the console default: TextIO.put("Option must be 1, 2, 3 or 4"); } } //end of userMenu /**main method, will call the userMenu and get the users choice and call * the relevant method to execute the users choice. */ public static void main(String[]args){ userMenu(); //calls the userMenu method } //end of main method /**instructions method, displays instructions on how to play * the game to the user/ */ public static void instructions(){ TextIO.putln("To beat the Maze Game you have to move your character"); TextIO.putln("through the maze and reach the exit in as few moves as possible."); TextIO.putln(""); TextIO.putln("Your characer is displayed as a " + PLAYER); TextIO.putln("The maze exit is displayed as a " + EXIT); TextIO.putln("Reach the exit and you have won escaped the maze."); TextIO.putln("To control your character type the direction you want to go"); TextIO.putln("and how many spaces you want to move"); TextIO.putln("for example 'D3' will move your character"); TextIO.putln("down 3 spaces."); TextIO.putln("Remember you can't walk through walls!"); boolean insOption; //boolean variable TextIO.putln(""); TextIO.put("Do you want to play the Maze Game now? (Y or N) "); insOption = TextIO.getlnBoolean(); if (insOption == true)playMazeGame(); else userMenu(); } //end of instructions method /**playMazeGame method, calls the loadMaze method and the charMove method * to start playing the Maze Game. */ public static void playMazeGame(){ loadMaze(); plyrMoves(); } //end of playMazeGame method /**loadMaze method, loads the 39x25 maze from the MazeGame.txt text file * and inserts values from the text file into the maze array and * displays the maze on screen using the unicode block characters. * plyrX and plyrY variables are set at their staring co ordinates so that when * a game is completed and the user selects to play a new game * the player character will always be at position 01. */ public static void loadMaze(){ plyrX = 0; plyrY = 1; TextIO.readFile("MazeGame.txt"); //now reads from the external MazeGame.txt file rows = TextIO.getInt(); //gets the number of rows from text file to create X dimensions cols = TextIO.getlnInt(); //gets number of columns from text file to create Y dimensions maze = new char[rows][cols]; //creates maze array of base type char with specified dimnensions //loop to process the array and read in values from the text file. for (int i = 0; i<rows; i++){ for (int j = 0; j<cols; j++){ maze[i][j] = TextIO.getChar(); } TextIO.getln(); } //end for loop TextIO.readStandardInput(); //closes MazeGame.txt file and reads from //standard input. //loop to process the array values and display as unicode characters for (int i = 0; i<rows; i++){ for (int j = 0; j<cols; j++){ if (i == plyrX && j == plyrY){ plyrX = i; plyrY = j; TextIO.put(PLAYER); //puts the player character at player co-ords } else{ if (maze[i][j] == '0') TextIO.putf("%c",WALL); //puts wall block if (maze[i][j] == '1') TextIO.putf("%c",PATH); //puts path block if (maze[i][j] == '2') { entX = i; entY = j; TextIO.putf("%c",ENTRANCE); //puts entrance character } if (maze[i][j] == '3') { exitX = i; //holds value of exit exitY = j; //co-ordinates TextIO.putf("%c",EXIT); //puts exit character } } } TextIO.putln(); } //end for loop } //end of loadMaze method /**redrawMaze method, method for redrawing the maze after each move. * */ public static void redrawMaze(){ TextIO.readFile("MazeGame.txt"); //now reads from the external MazeGame.txt file rows = TextIO.getInt(); //gets the number of rows from text file to create X dimensions cols = TextIO.getlnInt(); //gets number of columns from text file to create Y dimensions maze = new char[rows][cols]; //creates maze array of base type char with specified dimnensions //loop to process the array and read in values from the text file. for (int i = 0; i<rows; i++){ for (int j = 0; j<cols; j++){ maze[i][j] = TextIO.getChar(); } TextIO.getln(); } //end for loop TextIO.readStandardInput(); //closes MazeGame.txt file and reads from //standard input. //loop to process the array values and display as unicode characters for (int i = 0; i<rows; i++){ for (int j = 0; j<cols; j++){ if (i == plyrX && j == plyrY){ plyrX = i; plyrY = j; TextIO.put(PLAYER); //puts the player character at player co-ords } else{ if (maze[i][j] == '0') TextIO.putf("%c",WALL); //puts wall block if (maze[i][j] == '1') TextIO.putf("%c",PATH); //puts path block if (maze[i][j] == '2') { entX = i; entY = j; TextIO.putf("%c",ENTRANCE); //puts entrance character } if (maze[i][j] == '3') { exitX = i; //holds value of exit exitY = j; //co-ordinates TextIO.putf("%c",EXIT); //puts exit character } } } TextIO.putln(); } //end for loop } //end redrawMaze method /**replay game method * */ public static void replayGame(){ c_plyrX = plyrX; c_plyrY = plyrY; TextIO.readFile("MazeGame.txt"); //now reads from the external MazeGame.txt file rows = TextIO.getInt(); //gets the number of rows from text file to create X dimensions cols = TextIO.getlnInt(); //gets number of columns from text file to create Y dimensions mazeCopy = new char[rows][cols]; //creates maze array of base type char with specified dimnensions //loop to process the array and read in values from the text file. for (int i = 0; i<rows; i++){ for (int j = 0; j<cols; j++){ mazeCopy[i][j] = TextIO.getChar(); } TextIO.getln(); } //end for loop TextIO.readStandardInput(); //closes MazeGame.txt file and reads from //standard input. //loop to process the array values and display as unicode characters for (int i = 0; i<rows; i++){ for (int j = 0; j<cols; j++){ if (i == c_plyrX && j == c_plyrY){ c_plyrX = i; c_plyrY = j; TextIO.put(PLAYER); //puts the player character at player co-ords } else{ if (mazeCopy[i][j] == '0') TextIO.putf("%c",WALL); //puts wall block if (mazeCopy[i][j] == '1') TextIO.putf("%c",PATH); //puts path block if (mazeCopy[i][j] == '2') { entX = i; entY = j; TextIO.putf("%c",ENTRANCE); //puts entrance character } if (mazeCopy[i][j] == '3') { exitX = i; //holds value of exit exitY = j; //co-ordinates TextIO.putf("%c",EXIT); //puts exit character } } } TextIO.putln(); } //end for loop } //end replayGame method /**plyrMoves method, method for moving the players character * around the maze. */ public static void plyrMoves(){ int nplyrX = plyrX; int nplyrY = plyrY; int pMoves; direction(); //UP if (dir == 'U' || dir == 'u'){ nplyrX = plyrX; nplyrY = plyrY; for(pMoves = 0; pMoves <= spaces; pMoves++){ if (maze[nplyrX][nplyrY] == '0'){ TextIO.putln("Invalid move, try again."); } else if (pMoves != spaces){ nplyrX =plyrX + 1; } else { plyrX = plyrX-spaces; c_plyrX = plyrX; movesTaken++; } } }//end UP if //DOWN if (dir == 'D' || dir == 'd'){ nplyrX = plyrX; nplyrY = plyrY; for (pMoves = 0; pMoves <= spaces; pMoves ++){ if (maze[nplyrX][nplyrY] == '0'){ TextIO.putln("Invalid move, try again"); } else if (pMoves != spaces){ nplyrX = plyrX+1; } else{ plyrX = plyrX+spaces; c_plyrX = plyrX; movesTaken++; } } } //end DOWN if //LEFT if (dir == 'L' || dir =='l'){ nplyrX = plyrX; nplyrY = plyrY; for (pMoves = 0; pMoves <= spaces; pMoves++){ if (maze[nplyrX][nplyrY] == '0'){ TextIO.putln("Invalid move, try again"); } else if (pMoves != spaces){ nplyrY = plyrY + 1; } else{ plyrY = plyrY-spaces; c_plyrY = plyrY; movesTaken++; } } } //end LEFT if //RIGHT if (dir == 'R' || dir == 'r'){ nplyrX = plyrX; nplyrY = plyrY; for (pMoves = 0; pMoves <= spaces; pMoves++){ if (maze[nplyrX][nplyrY] == '0'){ TextIO.putln("Invalid move, try again."); } else if (pMoves != spaces){ nplyrY += 1; } else{ plyrY = plyrY+spaces; c_plyrY = plyrY; movesTaken++; } } } //end RIGHT if //prints message if player escapes from the maze. if (maze[plyrX][plyrY] == '3'){ TextIO.putln("****Congratulations****"); TextIO.putln(); TextIO.putln("You have escaped from the maze."); TextIO.putln(); userMenu(); } else{ movesTaken++; redrawMaze(); plyrMoves(); } } //end of plyrMoves method /**direction, method * */ public static char direction(){ TextIO.putln("Enter the direction you wish to move in and the distance"); TextIO.putln("i.e D3 = move down 3 spaces"); TextIO.putln("U - Up, D - Down, L - Left, R - Right: "); dir = TextIO.getChar(); if (dir =='U' || dir == 'D' || dir == 'L' || dir == 'R' || dir == 'u' || dir == 'd' || dir == 'l' || dir == 'r'){ spacesMoved(); } else{ loadMaze(); TextIO.putln("Invalid direction!"); TextIO.put("Direction must be one of U, D, L or R"); direction(); } return dir; //returns the value of dir (direction) } //end direction method /**spaces method, gets the amount of spaces the user wants to move * */ public static int spacesMoved(){ TextIO.putln(" "); spaces = TextIO.getlnInt(); if (spaces <= 0){ loadMaze(); TextIO.put("Invalid amount of spaces, try again"); spacesMoved(); } return spaces; } //end spacesMoved method } //end of MazeGame class

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Internet Explorer 8 Standards Mode Results In Broken Blank Page

- by Agent_9191

I'm running into a weird issue that I'm struggling to figure out what's causing the page to break. I have an internal website that's still under development (thus no link to the page) that works great in Firefox and Internet Explorer 8 in IE 7 Standards mode. But when I force it to IE 8 Standards mode the page will only display the title text in the browser tab and an otherwise completely blank page. It seems so broken that the blank page doesn't even have a context menu. The page generally looks like this: <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"> <html xmlns="http://www.w3.org/1999/xhtml"> <head> <meta content="IE=8" http-equiv="X-UA-Compatible" /> <title>Page Title</title> <link rel="shortcut icon" href="/Images/favicon.ico" type="image/x-icon" /> <link href="/Style/main.less" rel="stylesheet" type="text/css" /> </head> <body> ... </body> </html> You may notice the .less extension for the stylesheet. This is an ASP.NET MVC application and I'm making use of DotLess. I have the HttpHandler hooked up for it in the web.config. Of course there's some additional info on the page, but (in theory) it shouldn't be causing this issue. I've run the CSS and the HTML through the W3C validators and both have come back as completely valid. I'm trying the arduous task of removing/re-adding elements until it displays, but any insight into what could cause this would help. EDIT: it appears to be something related to the DotLess stylesheet. The resulting CSS is valid according to the W3C CSS validator. EDIT 2: Digging further, and making use of IE's Developer Tools to control the styles, it appears that IE is reading a single statement twice even though it only occurs once in the output. Here's the output of the Less file: a, abbr, acronym, address, applet, b, big, caption, center, cite, code, dd, dfn, div, dl, dt, em, fieldset, font, form, html, i, iframe, img, kbd, label, legend, li, object, pre, s, samp, small, span, strike, strong, sub, sup, tbody, td, tfoot, th, thead, tr, tt, u, var { margin: 0; padding: 0; border: 0; outline: 0; font-size: 100%; vertical-align: baseline; background: transparent; } blockquote, q { margin: 0; padding: 0; border: 0; outline: 0; font-size: 100%; vertical-align: baseline; background: transparent; quotes: none; } body { margin: 0; padding: 0; border: 0; outline: 0; font-size: 100%; vertical-align: baseline; line-height: 1; width: 100%; background: #efebde; min-width: 600px; } del { margin: 0; padding: 0; border: 0; outline: 0; font-size: 100%; vertical-align: baseline; background: transparent; text-decoration: line-through; } h1 { border: 0; outline: 0; vertical-align: baseline; background: transparent; font-size: 2em; margin: .8em 0 .2em 0; padding: 0; } h2 { border: 0; outline: 0; vertical-align: baseline; background: transparent; font-size: 1.8em; margin: .8em 0 .2em 0; padding: 0; } h3 { border: 0; outline: 0; vertical-align: baseline; background: transparent; font-size: 1.6em; margin: .8em 0 .2em 0; padding: 0; } h4 { margin: 0; padding: 0; border: 0; outline: 0; vertical-align: baseline; background: transparent; font-size: 1.4em; } h5 { margin: 0; padding: 0; border: 0; outline: 0; vertical-align: baseline; background: transparent; font-size: 1.2em; } h6 { margin: 0; padding: 0; border: 0; outline: 0; vertical-align: baseline; background: transparent; font-size: 1em; } ins { margin: 0; padding: 0; border: 0; outline: 0; font-size: 100%; vertical-align: baseline; background: transparent; text-decoration: none; } ol, ul { margin: 0; padding: 0; border: 0; outline: 0; font-size: 100%; vertical-align: baseline; background: transparent; list-style: none; } p { border: 0; outline: 0; font-size: 100%; vertical-align: baseline; background: transparent; margin: .4em 0 .8em 0; padding: 0; } table { margin: 0; padding: 0; border: 0; outline: 0; font-size: 100%; vertical-align: baseline; background: transparent; border-collapse: collapse; border-spacing: 0; } blockquote:before, blockquote:after, q:before, q:after { content: none; } :focus { outline: 0; } .bold { font-weight: bold; } .systemFont { font-family: Arial; } .labelled { font-style: italic; } .groovedBorder { border-color: #adaa9c; border-style: groove; border-width: medium; } #header, #footer { clear: both; float: left; width: 100%; } #header p, #header h1, #header h2 { padding: .4em 15px 0 15px; margin: 0; } #header ul { clear: left; float: left; width: 100%; list-style: none; margin: 10px 0 0 0; padding: 0; } #header ul li { display: inline; list-style: none; margin: 0; padding: 0; } #header ul li a { background: #eeeeee; display: block; float: left; left: 15px; line-height: 1.3em; margin: 0 0 0 1px; padding: 3px 10px; position: relative; text-align: center; text-decoration: none; } #header ul li a span { display: block; } #header ul li a:hover { background: #336699; } #header ul li a.active, #header ul li a.active:hover { background: black; font-weight: bold; } #header #logindisplay { float: right; padding-top: .5em; padding-bottom: .5em; padding-right: 1em; padding-left: 1em; } #title h1 { font-family: Arial; font-style: italic; font-size: 175%; text-align: center; margin-top: 1%; } .col1 { font-family: Arial; border-color: #adaa9c; border-style: groove; border-width: medium; min-height: 350px; float: left; overflow: hidden; position: relative; padding-top: 0; padding-bottom: 1em; padding-left: 0; padding-right: 0; } .col1 div.logo { text-align: center; } .col3 { font-family: Arial; border-color: #adaa9c; border-style: groove; border-width: medium; float: left; overflow: hidden; position: relative; } #layoutdims { clear: both; background: #eeeeee; margin: 0; padding: 6px 15px !important; text-align: right; } #company { padding-left: 10px; padding-top: 10px; margin: 0; } #company span { display: block; padding-left: 1em; } #version { padding-right: 1em; padding-top: 1em; text-align: center; } #menu li { padding: 6px; border-color: #adaa9c; border-style: groove; border-width: medium; min-width: 108px; } #menu li a.ciApp { text-decoration: none; font-size: 112.5%; font-weight: bold; font-family: Arial; color: black; } #menu li a.ciApp span { vertical-align: top; } .welcomemessage { font-size: 60.95%; } .newFeatures { overflow-y: scroll; max-height: 300px; } #newsfeed div .newsLabel { color: red; font-size: 60.95%; font-style: italic; } /************************************************************************************** This statement appears twice in Developer Tools. Disabling one disables both. Disabling it also causes the page to render. Turning it on and the page disappears again **************************************************************************************/ #newsfeed div .newFeatures { margin-left: 1em; margin-right: 1em; font-size: 60.95%; } /************************************************************************************** **************************************************************************************/ .colmask { clear: both; float: left; position: relative; overflow: hidden; width: 100%; } .colright, .colmid, .colleft { float: left; position: relative; width: 100%; } .col2 { float: left; overflow: hidden; position: relative; padding-top: 0; padding-bottom: 1em; padding-left: 0; padding-right: 0; } .threecol .colmid { right: 33%; } .threecol .colleft { right: 34%; } .threecol .col1 { width: 33%; left: 100%; } .threecol .col2 { width: 32%; left: 34%; } .threecol .col3 { width: 32%; left: 68.5%; } Notice the #newsfeed div .newFeatures identifier near the end. I don't know what's causing that as it's only appearing once in the output stream. Here's an image of it too: EDIT 3: It appears that even though it duplicates that particular selector, if I change the font-size to a whole number like 61% instead of the current 60.95% (that specific to defaultly match the existing desktop app as closely as possible) it works fine. So something specific to IE duplicating that selector block and the font-size being a percentage specific to two decimal places appears to kill IE8 Standards mode completely.

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Full Screen Video Tumblr

- by Kodi Lane

I have a tumblr theme seen on http://www.kodilane.com and i am trying to make my Video Posts full screen. I have tried editing the code but i can only get the pictures to stretch. I have attached the template i have so far, if you can spot the changes that need to be done to make the video posts stretch full screen like the pictures do i would really appreciate it. Thank You - Kodi <!DOCTYPE html> <html lang="en"> <head> <title>{Title} {block:PostSummary}- {PostSummary}{/block:PostSummary}</title> <link rel="shortcut icon" href="{Favicon}"> <link rel="alternate" type="application/rss+xml" href="{RSS}"> {block:Description} <meta name="description" content="{MetaDescription}" /> {/block:Description} <meta http-equiv="content-type" content="text/html; charset=utf-8" /> {block:Posts} <meta name="if:Reverse Description" content="0"/> <meta name="if:Include Attribution" content="1"/> <meta name="image:Background" content="http://static.tumblr.com/ffvtarv/QxLlmnswt/kims4.jpeg"/> <meta name="font:Body" content="Arial, Helvetica, sans"/> <meta name="color:Body Text" content="#fff"/> <meta name="color:Link" content="#d5d5d5"/> <meta name="color:Hover" content="#fff"/> <style type="text/css"> html, body, div, span, applet, object, iframe, h1, h2, h3, h4, h5, h6, p, blockquote, pre, a, abbr, acronym, address, big, cite, code, del, dfn, em, img, ins, kbd, q, s, samp, small, strike, strong, sub, sup, tt, var, b, u, i, center, dl, dt, dd, ol, ul, li, fieldset, form, label, legend, table, caption, tbody, tfoot, thead, tr, th, td, article, aside, canvas, details, embed, figure, figcaption, footer, header, hgroup, menu, nav, output, ruby, section, summary, time, mark, audio, video { margin: 0; padding: 0; border: 0; font-size: 100%; font: inherit; vertical-align: baseline; } /* HTML5 display-role reset for older browsers */ article, aside, details, figcaption, figure, footer, header, hgroup, menu, nav, section { display: block; } body { line-height: 1; font-family: {font:Body}; } ol, ul, .bigcats li { list-style: none; } .main ol{ list-style:decimal; margin-left:25px; margin-bottom:10px; } .main ul{ list-style: disc; margin-left:25px; margin-bottom:10px; } blockquote, q { quotes: none; font-style: italic; padding:7px 7px; display:block; } ol.notes blockquote a{ line-height:22px; } blockquote:before, blockquote:after, q:before, q:after { content: ''; content: none; } table { border-collapse: collapse; border-spacing: 0; } strong{ color:#9d9d9d; font-weight: bold; } em{ font-style: italic; } {block:IfNotReverseDescription} .article{ max-width:420px; position:fixed; bottom:43px; right:0; } {/block:IfNotReverseDescription} {block:IfReverseDescription} .article{ max-width:420px; position:fixed; bottom:43px; left:0; } {/block:IfReverseDescription} h1, h2{ position:absolute; top:-9999px; left:-9999px; } .nav{ width:100%; padding: 10px 0px 10px 0px; text-align:left; z-index: 10; color:{color:Link}; margin-left:5px; } .navwrap{ background-color:#000; position:fixed; width:100%; bottom:0px; clear:both; /* Firefox 3.6+ */ background: -moz-linear-gradient(left, rgba(0, 0, 0, .5), rgba(0, 0, 0, 0.8)); /* Safari 4-5, Chrome 1-9 */ background: -webkit-gradient(linear, left top, right top, from(rgba(0, 0, 0, .5)), to(rgba(0, 0, 0, 0.8))); /* Safari 5.1+, Chrome 10+ */ background: -webkit-linear-gradient(left, rgba(0, 0, 0, .5), rgba(0, 0, 0, 0.8)); /* Opera 11.10+ */ background: -o-linear-gradient(left, rgba(0, 0, 0, .5), rgba(0, 0, 0, 0.8)); padding-bottom:2px; box-shadow:0px 0px 3px #000000; } .nav ul li{ display:inline; font-size:13px; text-transform:uppercase; color:{color:Link}; list-style:none; text-align:center; } .nav li{ list-style: none; } .nav ul li a, .nav ul li a:visited { color:{color:Link}; padding: 10px 10px 3px 10px; } .nav ul li a:hover{ color:{color:Hover}; } a{ text-decoration:none; } .main a{ border-bottom: 1px {color:Link} dotted; color: {color:Link}; padding: 0 1px; } .main a:hover, .main a:focus{ color:{color:Hover}; border-bottom: transparent 1px solid; } a:visited, .main a:visited, { color: {color:Link}; } a:active {outline: none;} ol.notes, ol.notes li{ margin-bottom:2px; line-height:16px; } .audiometa{ padding-bottom:10px; } h3.push{ margin-bottom:10px; } h3{ margin-bottom:10px; } h3 a{ margin-bottom:10px; font-size:16px; color:{color:Hover}; } .main, .tags{ color:{color:Body Text}; display:block; padding: 15px; font-size: 12px; line-height: 16px; text-align: left; /* fallback */ background-color: #000; /* Firefox 3.6+ */ background: -moz-linear-gradient(left, rgba(0, 0, 0, .8), rgba(0, 0, 0, 0.6)); /* Safari 4-5, Chrome 1-9 */ background: -webkit-gradient(linear, left top, right top, from(rgba(0, 0, 0, .8)), to(rgba(0, 0, 0, 0.6))); /* Safari 5.1+, Chrome 10+ */ background: -webkit-linear-gradient(left, rgba(0, 0, 0, .8), rgba(0, 0, 0, 0.6)); /* Opera 11.10+ */ background: -o-linear-gradient(left, rgba(0, 0, 0, .8), rgba(0, 0, 0, 0.6)); margin-top:5px; box-shadow:0px 0px 3px #000000 } .tags{ padding: 5px 15px; padding-bottom:7px; } .main iframe, .main embed{ margin-left:-5px; margin-top:-5px; } a.more-link, .tags a, .meta a{ line-height:18px; font-size:10px; border-bottom: 1px #888 dotted; color: {color:Link}; padding: 0 1px; margin: 0 2px; } p.meta{ margin-bottom:5px; } .tags a:hover, a.more-link:hover{ color:{color:Hover}; border-bottom: 1px #FFF dotted; } .pagination{ color: {color:Body Text}; padding: 10px 15px; font-size: 10px; line-height: 16px; text-align: left; /* fallback */ background-color: #000; /* Firefox 3.6+ */ background: -moz-linear-gradient(left, rgba(0, 0, 0, .8), rgba(0, 0, 0, 0.6)); /* Safari 4-5, Chrome 1-9 */ background: -webkit-gradient(linear, left top, right top, from(rgba(0, 0, 0, .8)), to(rgba(0, 0, 0, 0.6))); /* Safari 5.1+, Chrome 10+ */ background: -webkit-linear-gradient(left, rgba(0, 0, 0, .8), rgba(0, 0, 0, 0.6)); /* Opera 11.10+ */ background: -o-linear-gradient(left, rgba(0, 0, 0, .8), rgba(0, 0, 0, 0.6)); margin-top:5px; box-shadow:0px 0px 3px #000000 } .pagination:hover{ /* Firefox 3.6+ */ background: -moz-linear-gradient(left, rgba(0, 0, 0, .6), rgba(0, 0, 0, 0.8)); /* Safari 4-5, Chrome 1-9 */ background: -webkit-gradient(linear, left top, right top, from(rgba(0, 0, 0, .6)), to(rgba(0, 0, 0, 0.8))); /* Safari 5.1+, Chrome 10+ */ background: -webkit-linear-gradient(left, rgba(0, 0, 0, .6), rgba(0, 0, 0, 0.8)); /* Opera 11.10+ */ background: -o-linear-gradient(left, rgba(0, 0, 0, .6), rgba(0, 0, 0, 0.8)); } #nextslide { width:48%; height:100%; background: url(http://static.tumblr.com/szanjxb/vI6lmo15u/forward.png) no-repeat right center, url(http://static.tumblr.com/ffvtarv/gemlmnsks/next-shadow.png) repeat-y right; position:fixed; top:0; right:0; float:left; opacity:0; filter:alpha(opacity=0); -webkit-transition: opacity .5s ease-out; -moz-transition: opacity .5s ease-out; -o-transition: opacity .5s ease-out; overflow:none; } p{ margin-bottom: 10px; } p:last-child{ margin-bottom: 0px; } #prevslide{ width:48%; float:left; height:100%; background: url(http://static.tumblr.com/szanjxb/MSClmo15g/back.png) no-repeat left center, url(http://static.tumblr.com/ffvtarv/bKulmnsl6/prev-shadow.png) repeat-y left; position:fixed; top: 0; left: 0; opacity:0; filter:alpha(opacity=0); -webkit-transition: opacity .5s ease-out; -moz-transition: opacity .5s ease-out; -o-transition: opacity .5s ease-out; } #nextslide:hover, #prevslide:hover{ filter:alpha(opacity=100); opacity:1.0; -webkit-transition: opacity .2s ease-out; -moz-transition: opacity .2s ease-out; -o-transition: opacity .2s ease-out; } p.time{ padding-bottom:10px; margin-bottom:10px; text-align: right; } .left{ float:left; } .right{ float:right; } .button{ position:fixed; bottom: 9px; right: 15px; line-height:12px; font-size:13px; color:{color:Link}; cursor: pointer; float:left; padding-bottom:1px; border-bottom: 2px solid transparent; } .button:hover{ color:{color:Link}; } .notes{ line-height: 11px; } ol.notes li{ list-style: none; } .clear { clear: both; display: block; overflow: hidden; visibility: hidden; width: 0; height: 0; } .hidden{ display:none; } {block:Photo} body {background: url({PhotoURL-HighRes}) no-repeat center center fixed black; -webkit-background-size: cover; -moz-background-size: cover; -o-background-size: cover; background-size: cover;} {/block:Photo} {block:Text} body {background: url({image:Background}) no-repeat center center fixed black; -webkit-background-size: cover; -moz-background-size: cover; -o-background-size: cover; background-size: cover;} {/block:Text} {block:Video} body {background: url({image:Background}) no-repeat center center fixed black; -webkit-background-size: cover; -moz-background-size: cover; -o-background-size: cover; background-size: cover;} {/block:Video} {block:Quote} body {background: url({image:Background}) no-repeat center center fixed black; -webkit-background-size: cover; -moz-background-size: cover; -o-background-size: cover; background-size: cover;} {/block:Quote} {block:Link} body {background: url({image:Background}) no-repeat center center fixed black; -webkit-background-size: cover; -moz-background-size: cover; -o-background-size: cover; background-size: cover;} {/block:Link} {block:Audio} body {background: url({image:Background}) no-repeat center center fixed black; -webkit-background-size: cover; -moz-background-size: cover; -o-background-size: cover; background-size: cover;} {block:AlbumArt} body{ background: url({AlbumArtURL}) no-repeat center center fixed black; -webkit-background-size: cover; -moz-background-size: cover; -o-background-size: cover; background-size: cover; } {/block:AlbumArt} {/block:Audio} {block:Answer} body {background: url({image:Background}) no-repeat center center fixed black; -webkit-background-size: cover; -moz-background-size: cover; -o-background-size: cover; background-size: cover;} {/block:Answer} {block:Chat} body {background: url({image:Background}) no-repeat center center fixed black; -webkit-background-size: cover; -moz-background-size: cover; -o-background-size: cover; background-size: cover;} {/block:Chat} {CustomCSS} </style> <script src="http://static.tumblr.com/ffvtarv/W6Llmnske/jquery-git.js"></script> <script src="http://static.tumblr.com/ffvtarv/QpUlmnsje/jquery.cookie.js"></script> <script> var uiStatus = $.cookie("uiStatus") $(document).ready(function(){ if(uiStatus == 'hidden') { $(".article,.navwrap").hide() }; $(".button").click(function () { $(".article,.navwrap").fadeToggle("slow", "swing"); if(uiStatus == 'hidden') { $.cookie("uiStatus", "visible"); } else { $.cookie("uiStatus", "hidden"); }; }); }); </script> </head> <h1><a href="/">{Title}</a></h1> <h2>{Description}</h2>  {block:Pagination} {block:PreviousPage} <a href="{PreviousPage}" title="Next Post"><div id="nextslide"></div></a> {/block:PreviousPage} {block:NextPage} <a href="{NextPage}" title="Previous Post"><div id="prevslide"></div></a> {/block:NextPage} {/block:Pagination} {block:PermalinkPagination} {block:PreviousPost} <a href="{PreviousPost}" title="Previous Post"><div id="prevslide"></div></a> {/block:PreviousPost} {block:NextPost} <a href="{NextPost}" title="Next Post"><div id="nextslide"></div></a> {/block:NextPost} {/block:PermalinkPagination} <div class="article"> {block:Pagination} {block:PreviousPage} <a href="{PreviousPage}" title="Newer Post"><div class="pagination">Newer Post</div></a> {/block:PreviousPage} {block:NextPage} <a href="{NextPage}" title="Older Post"><div class="pagination">Older Post</div></a> {/block:NextPage} {/block:Pagination} {block:PermalinkPagination} {block:NextPost} <a href="{NextPost}" title="Newer Post"><div class="pagination">Newer Post</div></a> {/block:NextPost} {block:PreviousPost} <a href="{PreviousPost}" title="Older Post"><div class="pagination">Older Post</div></a> {/block:PreviousPost} {/block:PermalinkPagination} {block:HasTags} <div class="tags"> {block:Tags} <a href="{TagURL}">{Tag}</a> {/block:Tags} </div> {/block:HasTags} <div class="main"> {block:Photo} {block:Caption} {Caption} {/block:Caption} {/block:Photo} {block:Video} {Video-400} {block:Caption} {Caption} {/block:Caption} {/block:Video} {block:Link} <h3><a href="{URL}" target="{Target}">{Name}</a></h3> {block:Description} {Description} {/block:Description} {/block:Link} {block:Quote} <h3>{Quote}</h3> {block:Source} <strong><p>{Source}</p></strong> {/block:Source} {/block:Quote} {block:Audio} {AudioPlayerBlack} <div class="audiometa"> {block:Artist} {Artist} {/block:Artist} {block:Album} {Album} {/block:Album} {block:TrackName} {TrackName} {/block:TrackName} </div> {block:Caption} {Caption} {/block:Caption} {/block:Audio} {block:Chat} <h3 class="push">{Title}</h3> {block:Lines} <p class="chat {Alt}"><strong>{block:Label}{Label}{/block:Label}</strong> {Line}</p> {/block:Lines} {/block:Chat} {block:Text} {Body} {block:Text} <p class="meta"> <a href="http://tmv.proto.jp/reblog.php?post_url={Permalink};" title="Reblog this" class="more-link left">Reblog</a> <span class="hidden">{block:Photo}{LinkOpenTag}Source{LinkCloseTag}{/block:Photo}</span> <a href="{Permalink}" title="Permalink{PhotoAlt}" class="more-link right notes">{NoteCountWithLabel}</a> </p> <div class="clear"></div> </div> </div> <script type="text/javascript"> document.onkeyup = KeyCheck; function KeyCheck(e) { var KeyID = (window.event) ? event.keyCode : e.keyCode; switch(KeyID) { {block:Pagination} {block:PreviousPage} case 39: window.location = "{PreviousPage}"; break; {/block:PreviousPage} {block:NextPage} case 37: window.location = "{NextPage}"; break; {/block:NextPage} {/block:Pagination} {block:PermalinkPagination} {block:PreviousPost} case 39: window.location = "{NextPost}"; break; {/block:PreviousPost} {block:NextPost} case 37: window.location = "{PreviousPost}"; break; {/block:NextPost} {/block:PermalinkPagination} } } </script> <div class="navwrap"> <div class="nav"> <ul> <li><a href="/" title="{Title}">KODI LANE</a></li> <li><a href="/archive" title="Archive of posts">Archive</a></li> {block:AskEnabled}<li><a href="/ask" title="Ask">{AskLabel}</a></li>{/block:AskEnabled} {block:SubmissionsEnabled}<li><a href="/submit" title="Submit">{SubmitLabel}</a></li>{/block:SubmissionsEnabled} {block:HasPages}{block:Pages}<li><a href="{URL}">{Label}</a></li>{/block:Pages}{/block:HasPages} {block:IfIncludeAttribution}<li><a href="http://jonathanhaggard.com/">Theme by Jon</a></li>{/block:IfIncludeAttribution} </ul> </div> </div> <div class="button">HIDE/SHOW UI</div> {/block:Posts}

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