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  • The way cores, processes, and threads work exactly?

    - by unknownthreat
    I need a bit of an advice for understanding how this whole procedure work exactly. If I am incorrect in any part described below, please correct me. In a single core CPU, it runs each process in the OS, jumping around from one process to another to utilize the best of itself. A process can also have many threads, in which the CPU core runs through these threads when it is running on the respective process. Now, on a multiple core CPU, Do the cores run in every process together, or can the cores run separately in different processes at one particular point of time? For instance, you have program A running two threads, can a duo core CPU run both threads of this program? I think the answer should be yes if we are using something like OpenMP. But while the cores are running in this OpenMP-embedded process, can one of the core simply switch to other process? For programs that are created for single core, when running at 100%, why the CPU utilization of each core are distributed? (ex. A duo core CPU of 80% and 20%. The utilization percentage of all cores always add up to 100% for this case.) Do the cores try help each other run each thread of each process in some ways? Frankly, I'm not sure how this works exactly. Any advice is appreciated.

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  • Parallel Processing Simulation in Javascript

    - by le_havre
    Hello, I'm new to JavaScript so forgive me for being a n00b. When there's intensive calculation required, it more than likely involves loops that are recursive or otherwise. Sometimes this may mean having am recursive loop that runs four functions and maybe each of those functions walks the entire DOM tree, read positions and do some math for collision detection or whatever. While the first function is walking the DOM tree, the next one will have to wait its for the first one to finish, and so forth. Instead of doing this, why not launch those loops-within-loops separately, outside the programs, and act on their calculations in another loop that runs slower because it isn't doing those calculations itself? Retarded or clever? Thanks in advance!

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  • Is NFS capable of preserving order of operations?

    - by JustJeff
    I have a diskless host 'A', that has a directory NFS mounted on server 'B'. A process on A writes to two files F1 and F2 in that directory, and a process on B monitors these files for changes. Assume that B polls for changes faster than A is expected to make them. Process A seeks the head of the files, writes data, and flushes. Process B seeks the head of the files and does reads. Are there any guarantees about how the order of the changes performed by A will be detected at B? Specifically, if A alternately writes to one file, and then the other, is it reasonable to expect that B will notice alternating changes to F1 and F2? Or could B conceivably detect a series of changes on F1 and then a series on F2? I know there are a lot of assumptions embedded in the question. For instance, I am virtually certain that, even operating on just one file, if A performs 100 operations on the file, B may see a smaller number of changes that give the same result, due to NFS caching some of the actions on A before they are communicated to B. And of course there would be issues with concurrent file access even if NFS weren't involved and both the reading and the writing process were running on the same real file system. The reason I'm even putting the question up here is that it seems like most of the time, the setup described above does detect the changes at B in the same order they are made at A, but that occasionally some events come through in transposed order. So, is it worth trying to make this work? Is there some way to tune NFS to make it work, perhaps cache settings or something? Or is fine-grained behavior like this just too much expect from NFS?

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  • Multiple python scripts sending messages to a single central script

    - by Ipsquiggle
    I have a number of scripts written in Python 2.6 that can be run arbitrarily. I would like to have a single central script that collects the output and displays it in a single log. Ideally it would satisfy these requirements: Every script sends its messages to the same "receiver" for display. If the receiver is not running when the first script tries to send a message, it is started. The receiver can also be launched and ended manually. (Though if ended, it will restart if another script tries to send a message.) The scripts can be run in any order, even simultaneously. Runs on Windows. Multiplatform is better, but at least it needs to work on Windows. I've come across some hints: os.pipe() multiprocess Occupying a port mutex From those pieces, I think I could cobble something together. Just wondering if there is an obviously 'right' way of doing this, or if I could learn from anyone's mistakes.

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  • Do really need a count lock on Multi threads with one CPU core?

    - by MrROY
    If i have some code looks like this(Please ignore the syntax, i want to understand it without a specified language): count = 0 def countDown(): count += 1 if __name__ == '__main__': thread1(countDown) thread2(countDown) thread3(countDown) Here i have a CPU with only one core, do i really need a lock to the variable count in case of it could be over-written by other threads. I don't know, but if the language cares a lot, please explain it under Java?C and Python, So many thanks.

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  • Storing task state between multiple django processes

    - by user366148
    I am building a logging-bridge between rabbitmq messages and Django application to store background task state in the database for further investigation/review, also to make it possible to re-publish tasks via the Django admin interface. I guess it's nothing fancy, just a standard Producer-Consumer pattern. Web application publishes to message queue and inserts initial task state into the database Consumer, which is a separate python process, handles the message and updates the task state depending on task output The problem is, some tasks are missing in the db and therefore never executed. I suspect it's because Consumer receives the message earlier than db commit is performed. So basically, returning from Model.save() doesn't mean the transaction has ended and the whole communication breaks. Is there any way I could fix this? Maybe some kind of post_transaction signal I could use? Thank you in advance.

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  • How to share a dictionary between multiple processes in python without locking

    - by RandomVector
    I need to share a huge dictionary (around 1 gb in size) between multiple processs, however since all processes will always read from it. I dont need locking. Is there any way to share a dictionary without locking? The multiprocessing module in python provides an Array class which allows sharing without locking by setting lock=false however There is no such option for Dictionary provided by manager in multiprocessing module.

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  • OSError : [Errno 38] Function not implemented - Django Celery implementation

    - by Jordan Messina
    I installed django-celery and I tried to start up the worker server but I get an OSError that a function isn't implemented. I'm running CentOS release 5.4 (Final) on a VPS: . broker -> amqp://guest@localhost:5672/ . queues -> . celery -> exchange:celery (direct) binding:celery . concurrency -> 4 . loader -> djcelery.loaders.DjangoLoader . logfile -> [stderr]@WARNING . events -> OFF . beat -> OFF [2010-07-22 17:10:01,364: WARNING/MainProcess] Traceback (most recent call last): [2010-07-22 17:10:01,364: WARNING/MainProcess] File "manage.py", line 11, in <module> [2010-07-22 17:10:01,364: WARNING/MainProcess] execute_manager(settings) [2010-07-22 17:10:01,364: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/django/core/management/__init__.py", line 438, in execute_manager [2010-07-22 17:10:01,364: WARNING/MainProcess] utility.execute() [2010-07-22 17:10:01,364: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/django/core/management/__init__.py", line 379, in execute [2010-07-22 17:10:01,365: WARNING/MainProcess] self.fetch_command(subcommand).run_from_argv(self.argv) [2010-07-22 17:10:01,365: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/django/core/management/base.py", line 191, in run_from_argv [2010-07-22 17:10:01,365: WARNING/MainProcess] self.execute(*args, **options.__dict__) [2010-07-22 17:10:01,365: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/django/core/management/base.py", line 218, in execute [2010-07-22 17:10:01,365: WARNING/MainProcess] output = self.handle(*args, **options) [2010-07-22 17:10:01,365: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/django_celery-2.0.0-py2.6.egg/djcelery/management/commands/celeryd.py", line 22, in handle [2010-07-22 17:10:01,366: WARNING/MainProcess] run_worker(**options) [2010-07-22 17:10:01,366: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/celery-2.0.1-py2.6.egg/celery/bin/celeryd.py", line 385, in run_worker [2010-07-22 17:10:01,366: WARNING/MainProcess] return Worker(**options).run() [2010-07-22 17:10:01,366: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/celery-2.0.1-py2.6.egg/celery/bin/celeryd.py", line 218, in run [2010-07-22 17:10:01,366: WARNING/MainProcess] self.run_worker() [2010-07-22 17:10:01,366: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/celery-2.0.1-py2.6.egg/celery/bin/celeryd.py", line 312, in run_worker [2010-07-22 17:10:01,367: WARNING/MainProcess] worker.start() [2010-07-22 17:10:01,367: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/celery-2.0.1-py2.6.egg/celery/worker/__init__.py", line 206, in start [2010-07-22 17:10:01,367: WARNING/MainProcess] component.start() [2010-07-22 17:10:01,367: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/celery-2.0.1-py2.6.egg/celery/concurrency/processes/__init__.py", line 54, in start [2010-07-22 17:10:01,367: WARNING/MainProcess] maxtasksperchild=self.maxtasksperchild) [2010-07-22 17:10:01,367: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/celery-2.0.1-py2.6.egg/celery/concurrency/processes/pool.py", line 448, in __init__ [2010-07-22 17:10:01,368: WARNING/MainProcess] self._setup_queues() [2010-07-22 17:10:01,368: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/celery-2.0.1-py2.6.egg/celery/concurrency/processes/pool.py", line 564, in _setup_queues [2010-07-22 17:10:01,368: WARNING/MainProcess] self._inqueue = SimpleQueue() [2010-07-22 17:10:01,368: WARNING/MainProcess] File "/usr/local/lib/python2.6/multiprocessing/queues.py", line 315, in __init__ [2010-07-22 17:10:01,368: WARNING/MainProcess] self._rlock = Lock() [2010-07-22 17:10:01,368: WARNING/MainProcess] File "/usr/local/lib/python2.6/multiprocessing/synchronize.py", line 117, in __init__ [2010-07-22 17:10:01,369: WARNING/MainProcess] SemLock.__init__(self, SEMAPHORE, 1, 1) [2010-07-22 17:10:01,369: WARNING/MainProcess] File "/usr/local/lib/python2.6/multiprocessing/synchronize.py", line 49, in __init__ [2010-07-22 17:10:01,369: WARNING/MainProcess] sl = self._semlock = _multiprocessing.SemLock(kind, value, maxvalue) [2010-07-22 17:10:01,369: WARNING/MainProcess] OSError [2010-07-22 17:10:01,369: WARNING/MainProcess] : [2010-07-22 17:10:01,369: WARNING/MainProcess] [Errno 38] Function not implemented Am I just totally screwed and should use a new kernel that has this implemented or is there an easy way to resolve this?

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  • Linux - preventing an application from failing due to lack of disk space [migrated]

    - by Jernej
    Due to an unpredicted scenario I am currently in need of finding a solution to the fact that an application (which I do not wish to kill) is slowly hogging the entire disk space. To give more context I have an application in Python that uses multiprocessing.Pool to start 5 threads. Each thread writes some data to its own file. The program is running on Linux and I do not have root access to the machine. The program is CPU intensive and has been running for months. It still has a few days to write all the data. 40% of the data in the files is redundant and can be removed after a quick test. The system on which the program is running only has 30GB of remaining disk space and at the current rate of work it will surely be hogged before the program finishes. Given the above points I see the following solutions with respective problems Given that the process number i is writing to file_i, is it safe to move file_i to an external location? Will the OS simply create a new instance of file_i and write to it? I assume moving the file would remove it and the process would end up writing to a "dead" file? Is there a "command line" way to stop 4 of the 5 spawned workers and wait until one of them finishes and then resume their work? (I am sure one single worker thread would avoid hogging the disk) Suppose I use CTRL+Z to freeze the main process. Will this stop all the other processes spawned by multiprocessing.Pool? If yes, can I then safely edit the files as to remove the redundant lines? Given the three options that I see, would any of them work in this context? If not, is there a better way to handle this problem? I would really like to avoid the scenario in which the program crashes just few days before its finish.

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  • How to migrate primary key generation from "increment" to "hi-lo"?

    - by Bevan
    I'm working with a moderate sized SQL Server 2008 database (around 120 tables, backups are around 4GB compressed) where all the table primary keys are declared as simple int columns. At present, primary key values are generated by NHibernate with the increment identity generator, which has worked well thus far, but precludes moving to a multiprocessing environment. Load on the system is growing, so I'm evaluating the work required to allow the use of multiple servers accessing a common database backend. Transitioning to the hi-lo generator seems to be the best way forward, but I can't find a lot of detail about how such a migration would work. Will NHibernate automatically create rows in the hi-lo table for me, or do I need to script these manually? If NHibernate does insert rows automatically, does it properly take account of existing key values? If NHibernate does take care of thing automatically, that's great. If not, are there any tools to help? Update NHibernate's increment identifier generator works entirely in-memory. It's seeded by selecting the maximum value of used identifiers from the table, but from that point on allocates new values by a simple increment, without reference back to the underlying database table. If any other process adds rows to the table, you end up with primary key collisions. You can run multiple threads within the one process just fine, but you can't run multiple processes. For comparison, the NHibernate identity generator works by configuring the database tables with identity columns, putting control over primary key generation in the hands of the database. This works well, but compromises the unit of work pattern. The hi-lo algorithm sits inbetween these - generation of primary keys is coordinated through the database, allowing for multiprocessing, but actual allocation can occur entirely in memory, avoiding problems with the unit of work pattern.

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  • gunicorn + django + nginx unix://socket failed (11: Resource temporarily unavailable)

    - by user1068118
    Running very high volume traffic on these servers configured with django, gunicorn, supervisor and nginx. But a lot of times I tend to see 502 errors. So I checked the nginx logs to see what error and this is what is recorded: [error] 2388#0: *208027 connect() to unix:/tmp/gunicorn-ourapp.socket failed (11: Resource temporarily unavailable) while connecting to upstream Can anyone help debug what might be causing this to happen? This is our nginx configuration: sendfile on; tcp_nopush on; tcp_nodelay off; listen 80 default_server; server_name imp.ourapp.com; access_log /mnt/ebs/nginx-log/ourapp-access.log; error_log /mnt/ebs/nginx-log/ourapp-error.log; charset utf-8; keepalive_timeout 60; client_max_body_size 8m; gzip_types text/plain text/xml text/css application/javascript application/x-javascript application/json; location / { proxy_pass http://unix:/tmp/gunicorn-ourapp.socket; proxy_pass_request_headers on; proxy_read_timeout 600s; proxy_connect_timeout 600s; proxy_redirect http://localhost/ http://imp.ourapp.com/; #proxy_set_header Host $host; #proxy_set_header X-Real-IP $remote_addr; #proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; #proxy_set_header X-Forwarded-Proto $my_scheme; #proxy_set_header X-Forwarded-Ssl $my_ssl; } We have configure Django to run in Gunicorn as a generic WSGI application. Supervisord is used to launch the gunicorn workers: home/user/virtenv/bin/python2.7 /home/user/virtenv/bin/gunicorn --config /home/user/shared/etc/gunicorn.conf.py daggr.wsgi:application This is what the gunicorn.conf.py looks like: import multiprocessing bind = 'unix:/tmp/gunicorn-ourapp.socket' workers = multiprocessing.cpu_count() * 3 + 1 timeout = 600 graceful_timeout = 40 Does anyone know where I can start digging to see what might be causing the problem? This is what my ulimit -a output looks like on the server: core file size (blocks, -c) 0 data seg size (kbytes, -d) unlimited scheduling priority (-e) 0 file size (blocks, -f) unlimited pending signals (-i) 59481 max locked memory (kbytes, -l) 64 max memory size (kbytes, -m) unlimited open files (-n) 50000 pipe size (512 bytes, -p) 8 POSIX message queues (bytes, -q) 819200 real-time priority (-r) 0 stack size (kbytes, -s) 8192 cpu time (seconds, -t) unlimited max user processes (-u) 1024 virtual memory (kbytes, -v) unlimited file locks (-x) unlimited

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  • Modern programming language with intuitive concurrent programming abstractions

    - by faif
    I am interested in learning concurrent programming, focusing on the application/user level (not system programming). I am looking for a modern high level programming language that provides intuitive abstractions for writing concurrent applications. I want to focus on languages that increase productivity and hide the complexity of concurrent programming. To give some examples, I don't consider a good option writing multithreaded code in C, C++, or Java because IMHO my productivity is reduced and their programming model is not intuitive. On the other hand, languages that increase productivity and offer more intuitive abstractions such as Python and the multiprocessing module, Erlang, Clojure, Scala, etc. would be good options. What would you recommend based on your experience and why?

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  • python, cluster computing, design help [closed]

    - by j dawg
    I would like to create my own parallel computing server. Can you please point me to some resources I can use to help me develop my server. Sorry, like I said I need help getting started. Yes, I am limited to python, I cannot use C. I am using a bunch of workstations and I want to use all the cpus in those machines. So what I am looking for is blog posts, books, articles that can help me develop my own client/server tools to send code from the client to the servers and spawn python processes based on the number of cpus. I know how to do the subprocessing/multiprocessing part of the program, I do not know how to create the server that will take the client's requests. I also need to figure out what is the best way to handle sending file data, like netcdf files or other spatial data. Any suggestions very welcome.

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  • PARTNER WEBCAST : Nimble SmartStack for Oracle with Cisco UCS (Nov 12)

    - by Zeynep Koch
    You are invited to the live webcast with Nimble Storage, Oracle and Cisco where we will talk about the new SmartStack solution from Nimble Storage that features Oracle Linux, Oracle VM and Cisco UCS products. When : Tuesday, November 12, 2013, 11:00 AM Pacific Time Panelists: Michele Resta, Director of Linux and Virtualization Alliances, Oracle John McAbel, Senior Product Manager, Cisco Ibby Rahmani, Solutions Marketing, Nimble Storage SmartStack™solutions provide pre-validated reference architectures that speed deployments and minimize risk. All SmartStack solutions incorporate Cisco UCS as the compute and network infrastructure. In this webinar, you will learn how Nimble Storage SmartStack with Oracle and Cisco provides a converged infrastructure for Oracle Database environments with Oracle Linux and Oracle VM. SmartStack, built on best-of-breed components, delivers the performance and reliability needed for deploying Oracle on a single symmetric multiprocessing (SMP) server or Oracle Real Application Clusters (RAC) on multiple nodes.  Register today 

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  • What is the *correct* term for a program that makes use of multiple hardware processor cores?

    - by Ryan Thompson
    I want to say that my program is capable of splitting some work across multiple CPU cores on a single system. What is the simple term for this? It's not multi-threaded, because that doesn't automatically imply that the threads run in parallel. It's not multi-process, because multiprocessing seems to be a property of a computer system, not a program. "capable of parallel operation" seems too wordy, and with all the confusion of terminology, I'm not even sure if it's accurate. So is there a simple term for this?

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  • Converting large files in python

    - by Cenoc
    I have a few files that are ~64GB in size that I think I would like to convert to hdf5 format. I was wondering what the best approach for doing so would be? Reading line-by-line seems to take more than 4 hours, so I was thinking of using multiprocessing in sequence, but was hoping for some direction on what would be the most efficient way without resorting to hadoop. Any help would be very much appreciated. (and thank you in advance) EDIT: Right now I'm just doing a for line in fd: approach. After that right now I just check to make sure I'm picking out the right sort of data, which is very short; I'm not writing anywhere, and it's taking around 4 hours to complete with that. I can't read blocks of data because the blocks in this weird file format I'm reading are not standard, it switches between three different sizes... and you can only tell which by reading the first few characters of the block.

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  • Any Open Source Pregel like framework for distributed processing of large Graphs?

    - by Akshay Bhat
    Google has described a novel framework for distributed processing on Massive Graphs. http://portal.acm.org/citation.cfm?id=1582716.1582723 I wanted to know if similar to Hadoop (Map-Reduce) are there any open source implementations of this framework? I am actually in process of writing a Pseudo distributed one using python and multiprocessing module and thus wanted to know if someone else has also tried implementing it. Since public information about this framework is extremely scarce. (A link above and a blog post at Google Research)

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  • PARTNER WEBCAST (June 4): Enhance Customer experience with Nimble Storage SmartStack for Oracle with Cisco

    - by Zeynep Koch
    Live Webcast: Enhance Customer experience with Nimble Storage SmartStack for Oracle with Cisco A webcast for resellers who sell Oracle workloads to customers  Wednesday, June 4, 2014, 8:00 AM PDT /11 AM EDT  Register today Nimble Storage SmartStack™ for Oracle provides pre-validated reference architecture that speed deployments and minimize risk.  IT and Oracle administrators and architects realize the importance of underlying Operating System, Virtualization software, and Storage in maintaining services levels and staying in budget.  In this webinar, you will learn how Nimble Storage SmartStack for Oracle provides a converged infrastructure for Oracle database online transaction processing (OLTP) and online analytical processing (OLAP) environments with Oracle Linux and Oracle VM. SmartStack delivers the performance and reliability needed for deploying Oracle on a single symmetric multiprocessing (SMP) server or if you are running Oracle Real Application Clusters (RAC) on multiple nodes. Nimble Storage SmartStack for Oracle with Cisco can help you provide: Improved Oracle performance Stress-free data protection and DR of your Oracle database Higher availability and uptime Accelerate Oracle development and improve testing All for dramatically less than what you’re paying now Presenters: Doan Nguyen, Senior Principal Product Marketing Director, Oracle Vanessa Scott , Business Development Manager, Cisco Ibrahim “Ibby” Rahmani, Product and Solutions Marketing, Nimble Storage Join this event to learn from our Nimble Storage and Oracle experts on how to optimize your customers' Oracle environments. Register today to learn more!

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  • PARTNER WEBCAST (June 4): Enhance Customer experience with Nimble Storage SmartStack for Oracle with Cisco

    - by Zeynep Koch
    Live Webcast: Enhance Customer experience with Nimble Storage SmartStack for Oracle with Cisco A webcast for resellers who sell Oracle workloads to customers  Wednesday, June 4, 2014, 8:00 AM PDT /11 AM EDT  Register today Nimble Storage SmartStack™ for Oracle provides pre-validated reference architecture that speed deployments and minimize risk.  IT and Oracle administrators and architects realize the importance of underlying Operating System, Virtualization software, and Storage in maintaining services levels and staying in budget.  In this webinar, you will learn how Nimble Storage SmartStack for Oracle provides a converged infrastructure for Oracle database online transaction processing (OLTP) and online analytical processing (OLAP) environments with Oracle Linux and Oracle VM. SmartStack delivers the performance and reliability needed for deploying Oracle on a single symmetric multiprocessing (SMP) server or if you are running Oracle Real Application Clusters (RAC) on multiple nodes. Nimble Storage SmartStack for Oracle with Cisco can help you provide: Improved Oracle performance Stress-free data protection and DR of your Oracle database Higher availability and uptime Accelerate Oracle development and improve testing All for dramatically less than what you’re paying now Presenters: Doan Nguyen, Senior Principal Product Marketing Director, Oracle Vanessa Scott , Business Development Manager, Cisco Ibrahim “Ibby” Rahmani, Product and Solutions Marketing, Nimble Storage Join this event to learn from our Nimble Storage and Oracle experts on how to optimize your customers' Oracle environments. Register today to learn more!

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  • DON'T MISS: Live Webcast - Nimble SmartStack for Oracle with Cisco UCS (Nov 12)

    - by Zeynep Koch
    You are invited to the live webcast with Nimble Storage, Oracle and Cisco where we will talk about the new SmartStack solution from Nimble Storage that features Oracle Linux, Oracle VM and Cisco UCS products. In this webinar, you will learn how Nimble Storage SmartStack with Oracle and Cisco provides a converged infrastructure for Oracle Database environments with Oracle Linux and Oracle VM. SmartStack, built on best-of-breed components, delivers the performance and reliability needed for deploying Oracle on a single symmetric multiprocessing (SMP) server or Oracle Real Application Clusters (RAC) on multiple nodes.  When : Tuesday, November 12, 2013, 11:00 AM Pacific Time Panelists: Michele Resta, Director of Linux and Virtualization Alliances, Oracle John McAbel, Senior Product Manager, Cisco Ibby Rahmani, Solutions Marketing, Nimble Storage SmartStack™solutions provide pre-validated reference architectures that speed deployments and minimize risk.      The pre-validated converged infrastructure is based on an Oracle Validated Configuration that includes Oracle Database and Oracle Linux with the Unbreakable Enterprise Kernel.     The solution components include a Nimble Storage CS-Series array, two Cisco UCS B200 M3 blade servers, Oracle Linux 6 Update 4 with the Unbreakable Enterprise Kernel, and Oracle Database 11g Release 2 or Oracle Database 12c Release 1.     The Nimble Storage CS-Series is certified with Oracle VM 3.2 providing an even more flexible solution leveraging virtualization for functions such as test and development by delivering excellent random I/O performance in Oracle VM environments. Register today 

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  • very quickly getting total size of folder

    - by freakazo
    I want to quickly find the total size of any folder using python. def GetFolderSize(path): TotalSize = 0 for item in os.walk(path): for file in item[2]: try: TotalSize = TotalSize + getsize(join(item[0], file)) except: print("error with file: " + join(item[0], file)) return TotalSize That's the simple script I wrote to get the total size of the folder, it took around 60 seconds (+-5 seconds). By using multiprocessing I got it down to 23 seconds on a quad core machine. Using the Windows file explorer it takes only ~3 seconds (Right click- properties to see for yourself). So is there a faster way of finding the total size of a folder close to the speed that windows can do it? Windows 7, python 2.6 (Did searches but most of the time people used a very similar method to my own) Thanks in advance.

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  • OpenCL: does it play well with OpenMP, can I connect other languages to it, etc.

    - by Cem Karan
    The 1.0 spec for OpenCL just came out a few days ago (Spec is here) and I've just started to read through it. I want to know if it plays well with other high performance multiprocessing APIs like OpenMP (spec) and I want to know what I should learn. So, here are my basic questions: If I am already using OpenMP, will that break OpenCL or vice-versa? Is OpenCL more powerful than OpenMP? Or are they intended to be complementary? Is there a standard way of connecting an OpenCL program to a standard C99 program (or any other language)? What is it? Does anyone know if anyone is writing an OpenCL book? I'm reading the spec, but I've found books to be more helpful.

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  • deciding between subprocess, multiprocesser and thread in Python?

    - by user248237
    I'd like to parallelize my Python program so that it can make use of multiple processors on the machine that it runs on. My parallelization is very simple, in that all the parallel "threads" of the program are independent and write their output to separate files. I don't need the threads to exchange information but it is imperative that I know when the threads finish since some steps of my pipeline depend on their output. Portability is important, in that I'd like this to run on any Python version on Mac, Linux and Windows. Given these constraints, which is the most appropriate Python module for implementing this? I am tryign to decide between thread, subprocess and multiprocessing, which all seem to provide related functionality. Any thoughts on this? I'd like the simplest solution that's portable. Thanks.

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  • Python: Plot some data (matplotlib) without GIL

    - by BandGap
    Hello all, my problem is the GIL of course. While I'm analysing data it would be nice to present some plots in between (so it's not too boring waiting for results) But the GIL prevents this (and this is bringing me to the point of asking myself if Python was such a good idea in the first place). I can only display the plot, wait till the user closes it and commence calculations after that. A waste of time obviously. I already tried the subprocess and multiprocessing modules but can't seem to get them to work. Any thoughts on this one? Thanks

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