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Search found 511 results on 21 pages for 'benchmark'.

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  • How to get the best LINPACK result and conquer the Top500?

    - by knweiss
    Given a large Linux HPC cluster with hundreds/thousands of nodes. What are your best practices to get the best possible LINPACK benchmark (HPL) result to submit for the Top500 supercomputer list? To give you an idea what kind of answers I would appreciate here are some sub-questions (with links): How to you tune the parameters (N, NB, P, Q, memory-alignment, etc) for the HPL.dat file (without spending too much time trying each possible permutation - esp with large problem sizes N)? Are there any Top500 submission rules to be aware of? What is allowed, what isn't? Which MPI product, which version? Does it make a difference? Any special host order in your MPI machine file? Do you use CPU pinning? How to you configure your interconnect? Which interconnect? Which BLAS package do you use for which CPU model? (Intel MKL, AMD ACML, GotoBLAS2, etc.) How do you prepare for the big run (on all nodes)? Start with small runs on a subset of nodes and then scale up? Is it really necessary to run LINPACK with a big run on all of the nodes (or is extrapolation allowed)? How do you optimize for the latest Intel/AMD CPUs? Hyperthreading? NUMA? Is it worth it to recompile the software stack or do you use precompiled binaries? Which settings? Which compiler optimizations, which compiler? (What about profile-based compilation?) How to get the best result given only a limited amount of time to do the benchmark run? (You can block a huge cluster forever) How do you prepare the individual nodes (stopping system daemons, freeing memory, etc)? How do you deal with hardware faults (ruining a huge run)? Are there any must-read documents or websites about this topic? E.g. I would love to hear about some background stories of some of the current Top500 systems and how they did their LINPACK benchmark. I deliberately don't want to mention concrete hardware details or discuss hardware recommendations because I don't want to limit the answers. However, feel free to mention hints e.g. for specific CPU models.

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  • SSD install - what do I need to watch out for when reconfiguring SATA ports?

    - by tim11g
    I installed a Samsung 840 SSD in a Windows 7 machine. It seems to be working fine, but I'm not seeing the expected performance. The AS SSD benchmark gives 76 for read and 138 for write. At the upper left of the benchmark it says "pciide - BAD" and "31K - BAD". I'm assuming the "pciide BAD" means the motherboard (Gigabyte GA-P35-DS4) is configured as IDE emulation and needs to change to native SATA. I don't know what the "31K" refers to. The bios settings look like this: I saw this article that indicates that changing the SATA mode of the boot drive can cause problems (Blue Screen): Error message occurs after you change the SATA mode of the boot drive What is the correct procedure to change the SATA Mode without causing a system failure? Apply the registry change from the MSFT article above first, then reboot and change the SATA mode? Will the SATA mode change in the BIOS affect other drives?

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  • Belarc Advisor (Store Passwords using Reversible Encryption)

    - by Steve
    Hi, I'm using Belarc Advisor to examine my PC. Part of BA is a security benchmark summary, which examines components of windows security and provides a benchmark rating. Two items are marked as Fail: - Store Passwords using Reversible Encryption - Password History Size I have opened the Local Security Settings tool from the Control Panel Administrative Tools, and ensured that the "Store passwords using reversible encryption" setting is enabled. Also, I've set the password history to a number. So I'm a bit miffed about the Fail marks. Any idea why the Fail marks appear? Any clues how I can Pass them? Thanks, Steve.

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  • 2010 MBP HD speed sanity check?

    - by hvgotcodes
    I have a 2010 MBP with the 7200 rpm hard drive. I was copying a 2.1GB file, and noticed read/write speeds of around 20MB/s. Is that reasonable? Seems slow to me.... What is the proper way to benchmark a HD on OS X? Googling I see xbench, but that hasn't been updated in years. I also see some guides for using the command line. The goal would be to benchmark my drive and then compare the results to some official scores that the drive should be getting.

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  • How come Core i7 (desktop) dominates Xeon (server)?

    - by grant tailor
    I have been using this performance benchmark results to select what CPUs to use on my web server and to my surprise, looks like Core i7 CPUs dominates the list pushing Xeon CPUs into the bush. Why is this? Why is Intel making the Core i7 perform better than the Xeon. Are Desktop CPUs supposed to perform better than server grade Xeon CPUs? I really don't get this and will like to know what you think or why this is so. Also I am thinking about getting a new web server and thinking between the i7-2600 VS the Xeon E3-1245. The i7-2600 is higher up in the performance benchmark but I am thinking the Xeon E3-1245 is server grade. What do you guys think? Should I go for the i7-2600? Or is the Xeon E3-1245 a server grade CPU for a reason?

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  • Why mysql 5.5 slower than 5.1 (linux,using mysqlslap)

    - by Zenofo
    my.cnf (5.5 and 5.1 is the same) : back_log=200 max_connections=512 max_connect_errors=999999 key_buffer=512M max_allowed_packet=8M table_cache=512 sort_buffer=8M read_buffer_size=8M thread_cache=8 thread_concurrency=4 myisam_sort_buffer_size=128M interactive_timeout=28800 wait_timeout=7200 mysql 5.5: ..mysql5.5/bin/mysqlslap -a --concurrency=10 --number-of-queries 5000 --iterations=5 -S /tmp/mysql_5.5.sock --engine=innodb Benchmark Running for engine innodb Average number of seconds to run all queries: 15.156 seconds Minimum number of seconds to run all queries: 15.031 seconds Maximum number of seconds to run all queries: 15.296 seconds Number of clients running queries: 10 Average number of queries per client: 500 mysql5.1: ..mysql5.5/bin/mysqlslap -a --concurrency=10 --number-of-queries 5000 --iterations=5 -S /tmp/mysql_5.1.sock --engine=innodb Benchmark Running for engine innodb Average number of seconds to run all queries: 13.252 seconds Minimum number of seconds to run all queries: 13.019 seconds Maximum number of seconds to run all queries: 13.480 seconds Number of clients running queries: 10 Average number of queries per client: 500 Why mysql 5.5 slower than 5.1 ? BTW:I'm tried mysql5.5/bin/mysqlslap and mysql5.1/bin/mysqlslap,result is the same

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  • How come i7 (desktop) dominates Xeon (server)?

    - by grant tailor
    I have been using this performance benchmark results http://www.cpubenchmark.net/high_end_cpus.html to select what CPUs to use on my web server and to my surprise...looks like i7 CPUs dominates the list pushing Xeon CPUs into the bush. Why is this? Why is Intel making the i7 perform better than the Xeon. Are Desktop CPUs supposed to perform better than server grade Xeon CPUs? I really don't get this and will like to know what you think or why this is so. Also i am thinking about getting a new web server and thinking between the i7-2600 VS the Xeon E3-1245. The i7-2600 is higher up in the performance benchmark but i am thinking the Xeon E3-1245 is server grade...so what do you guys think? Should i go for the i7-2600? Or is the Xeon E3-1245 a server grade CPU for a reason?

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  • Please help rails problem with stringify_keys error

    - by richard moss
    I have been trying to solve this for ages and can't figure it out. I have a form like so (taking out a lot of other fields) <% form_for @machine_enquiry, machine_enquiry_path(@machine_enquiry) do|me_form| %> <% me_form.fields_for :messages_attributes do |f| %> <%= f.text_field :title -%> <% end %> <%= me_form.submit 'Send message' %> <% end %> And an update action like @machine_enquiry = MachineEnquiry.find(params[:id]) @machine_enquiry.update_attributes(params[:machine_enquiry] And a machine_enquiry class like so: class MachineEnquiry < ActiveRecord::Base has_many :messages, :as => :messagable, :dependent => :destroy accepts_nested_attributes_for :messages end I am getting an error like so: NoMethodError in Machine enquiriesController#update undefined method `stringify_keys' for "2":String RAILS_ROOT: C:/INSTAN~2/rails_apps/Macrotec28th Application Trace | Framework Trace | Full Trace C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/nested_attributes.rb:294:in `assign_nested_attributes_for_collection_association' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/nested_attributes.rb:293:in `each' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/nested_attributes.rb:293:in `assign_nested_attributes_for_collection_association' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/nested_attributes.rb:215:in `messages_attributes=' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/base.rb:2745:in `send' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/base.rb:2745:in `attributes=' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/base.rb:2741:in `each' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/base.rb:2741:in `attributes=' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/base.rb:2627:in `update_attributes' C:/INSTAN~2/rails_apps/Macrotec28th/app/controllers/machine_enquiries_controller.rb:74:in `update' C:/INSTAN~2/rails_apps/Macrotec28th/app/controllers/machine_enquiries_controller.rb:72:in `update' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/nested_attributes.rb:294:in `assign_nested_attributes_for_collection_association' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/nested_attributes.rb:293:in `each' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/nested_attributes.rb:293:in `assign_nested_attributes_for_collection_association' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/nested_attributes.rb:215:in `messages_attributes=' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/base.rb:2745:in `send' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/base.rb:2745:in `attributes=' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/base.rb:2741:in `each' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/base.rb:2741:in `attributes=' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/base.rb:2627:in `update_attributes' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/mime_responds.rb:106:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/mime_responds.rb:106:in `respond_to' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/base.rb:1322:in `send' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/base.rb:1322:in `perform_action_without_filters' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/filters.rb:617:in `call_filters' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/filters.rb:610:in `perform_action_without_benchmark' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/benchmarking.rb:68:in `perform_action_without_rescue' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activesupport-2.3.2/lib/active_support/core_ext/benchmark.rb:17:in `ms' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activesupport-2.3.2/lib/active_support/core_ext/benchmark.rb:10:in `realtime' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activesupport-2.3.2/lib/active_support/core_ext/benchmark.rb:17:in `ms' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/benchmarking.rb:68:in `perform_action_without_rescue' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/rescue.rb:160:in `perform_action_without_flash' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/flash.rb:141:in `perform_action' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/base.rb:523:in `send' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/base.rb:523:in `process_without_filters' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/filters.rb:606:in `process' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/base.rb:391:in `process' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/base.rb:386:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/routing/route_set.rb:433:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/dispatcher.rb:88:in `dispatch' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/dispatcher.rb:111:in `_call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/dispatcher.rb:82:in `initialize' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/query_cache.rb:29:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/query_cache.rb:29:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/connection_adapters/abstract/query_cache.rb:34:in `cache' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/query_cache.rb:9:in `cache' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/query_cache.rb:28:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/connection_adapters/abstract/connection_pool.rb:361:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/vendor/rack-1.0/rack/head.rb:9:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/vendor/rack-1.0/rack/methodoverride.rb:24:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/params_parser.rb:15:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/rewindable_input.rb:25:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/session/cookie_store.rb:93:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/reloader.rb:9:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/failsafe.rb:11:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/vendor/rack-1.0/rack/lock.rb:11:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/vendor/rack-1.0/rack/lock.rb:11:in `synchronize' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/vendor/rack-1.0/rack/lock.rb:11:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/dispatcher.rb:106:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/cgi_process.rb:44:in `dispatch_cgi' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/dispatcher.rb:102:in `dispatch_cgi' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/dispatcher.rb:28:in `dispatch' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel/rails.rb:76:in `process' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel/rails.rb:74:in `synchronize' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel/rails.rb:74:in `process' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel.rb:159:in `process_client' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel.rb:158:in `each' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel.rb:158:in `process_client' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel.rb:285:in `run' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel.rb:285:in `initialize' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel.rb:285:in `new' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel.rb:285:in `run' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel.rb:268:in `initialize' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel.rb:268:in `new' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel.rb:268:in `run' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel/configurator.rb:282:in `run' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel/configurator.rb:281:in `each' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel/configurator.rb:281:in `run' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/bin/mongrel_rails:128:in `run' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel/command.rb:212:in `run' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/bin/mongrel_rails:281 C:/INSTAN~2/ruby/bin/mongrel_rails:19:in `load' C:/INSTAN~2/ruby/bin/mongrel_rails:19 C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/nested_attributes.rb:294:in `assign_nested_attributes_for_collection_association' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/nested_attributes.rb:293:in `each' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/nested_attributes.rb:293:in `assign_nested_attributes_for_collection_association' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/nested_attributes.rb:215:in `messages_attributes=' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/base.rb:2745:in `send' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/base.rb:2745:in `attributes=' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/base.rb:2741:in `each' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/base.rb:2741:in `attributes=' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/base.rb:2627:in `update_attributes' C:/INSTAN~2/rails_apps/Macrotec28th/app/controllers/machine_enquiries_controller.rb:74:in `update' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/mime_responds.rb:106:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/mime_responds.rb:106:in `respond_to' C:/INSTAN~2/rails_apps/Macrotec28th/app/controllers/machine_enquiries_controller.rb:72:in `update' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/base.rb:1322:in `send' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/base.rb:1322:in `perform_action_without_filters' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/filters.rb:617:in `call_filters' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/filters.rb:610:in `perform_action_without_benchmark' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/benchmarking.rb:68:in `perform_action_without_rescue' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activesupport-2.3.2/lib/active_support/core_ext/benchmark.rb:17:in `ms' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activesupport-2.3.2/lib/active_support/core_ext/benchmark.rb:10:in `realtime' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activesupport-2.3.2/lib/active_support/core_ext/benchmark.rb:17:in `ms' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/benchmarking.rb:68:in `perform_action_without_rescue' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/rescue.rb:160:in `perform_action_without_flash' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/flash.rb:141:in `perform_action' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/base.rb:523:in `send' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/base.rb:523:in `process_without_filters' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/filters.rb:606:in `process' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/base.rb:391:in `process' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/base.rb:386:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/routing/route_set.rb:433:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/dispatcher.rb:88:in `dispatch' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/dispatcher.rb:111:in `_call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/dispatcher.rb:82:in `initialize' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/query_cache.rb:29:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/query_cache.rb:29:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/connection_adapters/abstract/query_cache.rb:34:in `cache' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/query_cache.rb:9:in `cache' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/query_cache.rb:28:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/activerecord-2.3.2/lib/active_record/connection_adapters/abstract/connection_pool.rb:361:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/vendor/rack-1.0/rack/head.rb:9:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/vendor/rack-1.0/rack/methodoverride.rb:24:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/params_parser.rb:15:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/rewindable_input.rb:25:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/session/cookie_store.rb:93:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/reloader.rb:9:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/failsafe.rb:11:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/vendor/rack-1.0/rack/lock.rb:11:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/vendor/rack-1.0/rack/lock.rb:11:in `synchronize' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/vendor/rack-1.0/rack/lock.rb:11:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/dispatcher.rb:106:in `call' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/cgi_process.rb:44:in `dispatch_cgi' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/dispatcher.rb:102:in `dispatch_cgi' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/actionpack-2.3.2/lib/action_controller/dispatcher.rb:28:in `dispatch' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel/rails.rb:76:in `process' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel/rails.rb:74:in `synchronize' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel/rails.rb:74:in `process' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel.rb:159:in `process_client' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel.rb:158:in `each' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel.rb:158:in `process_client' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel.rb:285:in `run' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel.rb:285:in `initialize' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel.rb:285:in `new' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel.rb:285:in `run' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel.rb:268:in `initialize' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel.rb:268:in `new' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel.rb:268:in `run' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel/configurator.rb:282:in `run' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel/configurator.rb:281:in `each' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel/configurator.rb:281:in `run' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/bin/mongrel_rails:128:in `run' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/lib/mongrel/command.rb:212:in `run' C:/INSTAN~2/ruby/lib/ruby/gems/1.8/gems/mongrel-1.1.2-x86-mswin32/bin/mongrel_rails:281 C:/INSTAN~2/ruby/bin/mongrel_rails:19:in `load' C:/INSTAN~2/ruby/bin/mongrel_rails:19 Request Parameters: {"commit"=>"Send message", "_method"=>"put", "machine_enquiry"=>{"messages_attributes"=>{"message"=>"2", "title"=>"1", "message_type_id"=>"1", "contact_detail_ids"=>["1", "11"]}}, "id"=>"2", "datetime"=>""} Why am I getting this error? Can anyone help with this?

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  • IBM "per core" comparisons for SPECjEnterprise2010

    - by jhenning
    I recently stumbled upon a blog entry from Roman Kharkovski (an IBM employee) comparing some SPECjEnterprise2010 results for IBM vs. Oracle. Mr. Kharkovski's blog claims that SPARC delivers half the transactions per core vs. POWER7. Prior to any argument, I should say that my predisposition is to like Mr. Kharkovski, because he says that his blog is intended to be factual; that the intent is to try to avoid marketing hype and FUD tactic; and mostly because he features a picture of himself wearing a bike helmet (me too). Therefore, in a spirit of technical argument, rather than FUD fight, there are a few areas in his comparison that should be discussed. Scaling is not free For any benchmark, if a small system scores 13k using quantity R1 of some resource, and a big system scores 57k using quantity R2 of that resource, then, sure, it's tempting to divide: is  13k/R1 > 57k/R2 ? It is tempting, but not necessarily educational. The problem is that scaling is not free. Building big systems is harder than building small systems. Scoring  13k/R1  on a little system provides no guarantee whatsoever that one can sustain that ratio when attempting to handle more than 4 times as many users. Choosing the denominator radically changes the picture When ratios are used, one can vastly manipulate appearances by the choice of denominator. In this case, lots of choices are available for the resource to be compared (R1 and R2 above). IBM chooses to put cores in the denominator. Mr. Kharkovski provides some reasons for that choice in his blog entry. And yet, it should be noted that the very concept of a core is: arbitrary: not necessarily comparable across vendors; fluid: modern chips shift chip resources in response to load; and invisible: unless you have a microscope, you can't see it. By contrast, one can actually see processor chips with the naked eye, and they are a bit easier to count. If we put chips in the denominator instead of cores, we get: 13161.07 EjOPS / 4 chips = 3290 EjOPS per chip for IBM vs 57422.17 EjOPS / 16 chips = 3588 EjOPS per chip for Oracle The choice of denominator makes all the difference in the appearance. Speaking for myself, dividing by chips just seems to make more sense, because: I can see chips and count them; and I can accurately compare the number of chips in my system to the count in some other vendor's system; and Tthe probability of being able to continue to accurately count them over the next 10 years of microprocessor development seems higher than the probability of being able to accurately and comparably count "cores". SPEC Fair use requirements Speaking as an individual, not speaking for SPEC and not speaking for my employer, I wonder whether Mr. Kharkovski's blog article, taken as a whole, meets the requirements of the SPEC Fair Use rule www.spec.org/fairuse.html section I.D.2. For example, Mr. Kharkovski's footnote (1) begins Results from http://www.spec.org as of 04/04/2013 Oracle SUN SPARC T5-8 449 EjOPS/core SPECjEnterprise2010 (Oracle's WLS best SPECjEnterprise2010 EjOPS/core result on SPARC). IBM Power730 823 EjOPS/core (World Record SPECjEnterprise2010 EJOPS/core result) The questionable tactic, from a Fair Use point of view, is that there is no such metric at the designated location. At www.spec.org, You can find the SPEC metric 57422.17 SPECjEnterprise2010 EjOPS for Oracle and You can also find the SPEC metric 13161.07 SPECjEnterprise2010 EjOPS for IBM. Despite the implication of the footnote, you will not find any mention of 449 nor anything that says 823. SPEC says that you can, under its fair use rule, derive your own values; but it emphasizes: "The context must not give the appearance that SPEC has created or endorsed the derived value." Substantiation and transparency Although SPEC disclaims responsibility for non-SPEC information (section I.E), it says that non-SPEC data and methods should be accurate, should be explained, should be substantiated. Unfortunately, it is difficult or impossible for the reader to independently verify the pricing: Were like units compared to like (e.g. list price to list price)? Were all components (hw, sw, support) included? Were all fees included? Note that when tpc.org shows IBM pricing, there are often items such as "PROCESSOR ACTIVATION" and "MEMORY ACTIVATION". Without the transparency of a detailed breakdown, the pricing claims are questionable. T5 claim for "Fastest Processor" Mr. Kharkovski several times questions Oracle's claim for fastest processor, writing You see, when you publish industry benchmarks, people may actually compare your results to other vendor's results. Well, as we performance people always say, "it depends". If you believe in performance-per-core as the primary way of looking at the world, then yes, the POWER7+ is impressive, spending its chip resources to support up to 32 threads (8 cores x 4 threads). Or, it just might be useful to consider performance-per-chip. Each SPARC T5 chip allows 128 hardware threads to be simultaneously executing (16 cores x 8 threads). The Industry Standard Benchmark that focuses specifically on processor chip performance is SPEC CPU2006. For this very well known and popular benchmark, SPARC T5: provides better performance than both POWER7 and POWER7+, for 1 chip vs. 1 chip, for 8 chip vs. 8 chip, for integer (SPECint_rate2006) and floating point (SPECfp_rate2006), for Peak tuning and for Base tuning. For example, at the 8-chip level, integer throughput (SPECint_rate2006) is: 3750 for SPARC 2170 for POWER7+. You can find the details at the March 2013 BestPerf CPU2006 page SPEC is a trademark of the Standard Performance Evaluation Corporation, www.spec.org. The two specific results quoted for SPECjEnterprise2010 are posted at the URLs linked from the discussion. Results for SPEC CPU2006 were verified at spec.org 1 July 2013, and can be rechecked here.

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  • Take Two: Comparing JVMs on ARM/Linux

    - by user12608080
    Although the intent of the previous article, entitled Comparing JVMs on ARM/Linux, was to introduce and highlight the availability of the HotSpot server compiler (referred to as c2) for Java SE-Embedded ARM v7,  it seems, based on feedback, that everyone was more interested in the OpenJDK comparisons to Java SE-E.  In fact there were two main concerns: The fact that the previous article compared Java SE-E 7 against OpenJDK 6 might be construed as an unlevel playing field because version 7 is newer and therefore potentially more optimized. That the generic compiler settings chosen to build the OpenJDK implementations did not put those versions in a particularly favorable light. With those considerations in mind, we'll institute the following changes to this version of the benchmarking: In order to help alleviate an additional concern that there is some sort of benchmark bias, we'll use a different suite, called DaCapo.  Funded and supported by many prestigious organizations, DaCapo's aim is to benchmark real world applications.  Further information about DaCapo can be found at http://dacapobench.org. At the suggestion of Xerxes Ranby, who has been a great help through this entire exercise, a newer Linux distribution will be used to assure that the OpenJDK implementations were built with more optimal compiler settings.  The Linux distribution in this instance is Ubuntu 11.10 Oneiric Ocelot. Having experienced difficulties getting Ubuntu 11.10 to run on the original D2Plug ARMv7 platform, for these benchmarks, we'll switch to an embedded system that has a supported Ubuntu 11.10 release.  That platform is the Freescale i.MX53 Quick Start Board.  It has an ARMv7 Coretex-A8 processor running at 1GHz with 1GB RAM. We'll limit comparisons to 4 JVM implementations: Java SE-E 7 Update 2 c1 compiler (default) Java SE-E 6 Update 30 (c1 compiler is the only option) OpenJDK 6 IcedTea6 1.11pre 6b23~pre11-0ubuntu1.11.10.2 CACAO build 1.1.0pre2 OpenJDK 6 IcedTea6 1.11pre 6b23~pre11-0ubuntu1.11.10.2 JamVM build-1.6.0-devel Certain OpenJDK implementations were eliminated from this round of testing for the simple reason that their performance was not competitive.  The Java SE 7u2 c2 compiler was also removed because although quite respectable, it did not perform as well as the c1 compilers.  Recall that c2 works optimally in long-lived situations.  Many of these benchmarks completed in a relatively short period of time.  To get a feel for where c2 shines, take a look at the first chart in this blog. The first chart that follows includes performance of all benchmark runs on all platforms.  Later on we'll look more at individual tests.  In all runs, smaller means faster.  The DaCapo aficionado may notice that only 10 of the 14 DaCapo tests for this version were executed.  The reason for this is that these 10 tests represent the only ones successfully completed by all 4 JVMs.  Only the Java SE-E 6u30 could successfully run all of the tests.  Both OpenJDK instances not only failed to complete certain tests, but also experienced VM aborts too. One of the first observations that can be made between Java SE-E 6 and 7 is that, for all intents and purposes, they are on par with regards to performance.  While it is a fact that successive Java SE releases add additional optimizations, it is also true that Java SE 7 introduces additional complexity to the Java platform thus balancing out any potential performance gains at this point.  We are still early into Java SE 7.  We would expect further performance enhancements for Java SE-E 7 in future updates. In comparing Java SE-E to OpenJDK performance, among both OpenJDK VMs, Cacao results are respectable in 4 of the 10 tests.  The charts that follow show the individual results of those four tests.  Both Java SE-E versions do win every test and outperform Cacao in the range of 9% to 55%. For the remaining 6 tests, Java SE-E significantly outperforms Cacao in the range of 114% to 311% So it looks like OpenJDK results are mixed for this round of benchmarks.  In some cases, performance looks to have improved.  But in a majority of instances, OpenJDK still lags behind Java SE-Embedded considerably. Time to put on my asbestos suit.  Let the flames begin...

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  • Mongodb performance on Windows

    - by Chris
    I've been researching nosql options available for .NET lately and MongoDB is emerging as a clear winner in terms of availability and support, so tonight I decided to give it a go. I downloaded version 1.2.4 (Windows x64 binary) from the mongodb site and ran it with the following options: C:\mongodb\bin>mkdir data C:\mongodb\bin>mongod -dbpath ./data --cpu --quiet I then loaded up the latest mongodb-csharp driver from http://github.com/samus/mongodb-csharp and immediately ran the benchmark program. Having heard about how "amazingly fast" MongoDB is, I was rather shocked at the poor benchmark performance. Starting Tests encode (small).........................................320000 00:00:00.0156250 encode (medium)........................................80000 00:00:00.0625000 encode (large).........................................1818 00:00:02.7500000 decode (small).........................................320000 00:00:00.0156250 decode (medium)........................................160000 00:00:00.0312500 decode (large).........................................2370 00:00:02.1093750 insert (small, no index)...............................2176 00:00:02.2968750 insert (medium, no index)..............................2269 00:00:02.2031250 insert (large, no index)...............................778 00:00:06.4218750 insert (small, indexed)................................2051 00:00:02.4375000 insert (medium, indexed)...............................2133 00:00:02.3437500 insert (large, indexed)................................835 00:00:05.9843750 batch insert (small, no index).........................53333 00:00:00.0937500 batch insert (medium, no index)........................26666 00:00:00.1875000 batch insert (large, no index).........................1114 00:00:04.4843750 find_one (small, no index).............................350 00:00:14.2812500 find_one (medium, no index)............................204 00:00:24.4687500 find_one (large, no index).............................135 00:00:37.0156250 find_one (small, indexed)..............................352 00:00:14.1718750 find_one (medium, indexed).............................184 00:00:27.0937500 find_one (large, indexed)..............................128 00:00:38.9062500 find (small, no index).................................516 00:00:09.6718750 find (medium, no index)................................316 00:00:15.7812500 find (large, no index).................................216 00:00:23.0468750 find (small, indexed)..................................532 00:00:09.3906250 find (medium, indexed).................................346 00:00:14.4375000 find (large, indexed)..................................212 00:00:23.5468750 find range (small, indexed)............................440 00:00:11.3593750 find range (medium, indexed)...........................294 00:00:16.9531250 find range (large, indexed)............................199 00:00:25.0625000 Press any key to continue... For starters, I can get better non-batch insert performance from SQL Server Express. What really struck me, however, was the slow performance of the find_nnnn queries. Why is retrieving data from MongoDB so slow? What am I missing? Edit: This was all on the local machine, no network latency or anything. MongoDB's CPU usage ran at about 75% the entire time the test was running. Edit 2: Also, I ran a trace on the benchmark program and confirmed that 50% of the CPU time spent was waiting for MongoDB to return data, so it's not a performance issue with the C# driver.

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  • pathinfo vs fnmatch

    - by zaf
    There was a small debate regarding the speed of fnmatch over pathinfo here : http://stackoverflow.com/questions/2692536/how-to-check-if-file-is-php I wasn't totally convinced so decided to benchmark the two functions. Using dynamic and static paths showed that pathinfo was faster. Is my benchmarking logic and conclusion valid? I include a sample of the results which are in seconds for 100,000 iterations on my machine : dynamic path pathinfo 3.79311800003 fnmatch 5.10071492195 x1.34 static path pathinfo 1.03921294212 fnmatch 2.37709188461 x2.29 Code: <pre> <?php $iterations=100000; // Benchmark with dynamic file path print("dynamic path\n"); $i=$iterations; $t1=microtime(true); while($i-->0){ $f='/'.uniqid().'/'.uniqid().'/'.uniqid().'/'.uniqid().'.php'; if(pathinfo($f,PATHINFO_EXTENSION)=='php') $d=uniqid(); } $t2=microtime(true) - $t1; print("pathinfo $t2\n"); $i=$iterations; $t1=microtime(true); while($i-->0){ $f='/'.uniqid().'/'.uniqid().'/'.uniqid().'/'.uniqid().'.php'; if(fnmatch('*.php',$f)) $d=uniqid(); } $t3 = microtime(true) - $t1; print("fnmatch $t3\n"); print('x'.round($t3/$t2,2)."\n\n"); // Benchmark with static file path print("static path\n"); $f='/'.uniqid().'/'.uniqid().'/'.uniqid().'/'.uniqid().'.php'; $i=$iterations; $t1=microtime(true); while($i-->0) if(pathinfo($f,PATHINFO_EXTENSION)=='php') $d=uniqid(); $t2=microtime(true) - $t1; print("pathinfo $t2\n"); $i=$iterations; $t1=microtime(true); while($i-->0) if(fnmatch('*.php',$f)) $d=uniqid(); $t3=microtime(true) - $t1; print("fnmatch $t3\n"); print('x'.round($t3/$t2,2)."\n\n"); ?> </pre>

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  • assistance with classifying tests

    - by amateur
    I have a .net c# library that I have created that I am currently creating some unit tests for. I am at present writing unit tests for a cache provider class that I have created. Being new to writing unit tests I have 2 questions These being: My cache provider class is the abstraction layer to my distributed cache - AppFabric. So to test aspects of my cache provider class such as adding to appfabric cache, removing from cache etc involves communicating with appfabric. Therefore the tests to test for such, are they still categorised as unit tests or integration tests? The above methods I am testing due to interacting with appfabric, I would like to time such methods. If they take longer than a specified benchmark, the tests have failed. Again I ask the question, can this performance benchmark test be classifed as a unit test? The way I have my tests set up I want to include all unit tests together, integration tests together etc, therefore I ask these questions that I would appreciate input on.

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  • Mathematica & J/Link: Memory Constraints?

    - by D-Bug
    I am doing a computing-intensive benchmark using Mathematica and its J/Link Java interface. The benchmark grinds to a halt if a memory footprint of about 320 MB is reached, since this seems to be the limit and the garbage collector needs more and more time and will eventually fail. The Mathematica function ReinstallJava takes the argument command line. I tried to do ReinstallJava[CommandLine -> "java -Xmx2000m ..."] but Mathematica seems to ignore the -Xmx option completely. How can I set the -Xmx memory option for my java program? Where does the limit of 320 MB come from? Any help would be greatly appreciated.

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  • Why PHP (script) serves more requests than CGI (compiled)?

    - by Lucas Batistussi
    I developed the following CGI script and run on Apache 2 (http://localhost/test.chtml). I did same script in PHP (http://localhost/verifica.php). Later I performed Apache benchmark using Apache Benchmark tool. The results are showed in images. include #include <stdlib.h> int main(void) { printf("%s%c%c\n", "Content-Type:text/html;charset=iso-8859-1",13,10); printf("<TITLE>Multiplication results</TITLE>\n"); printf("<H3>Multiplication results</H3>\n"); return 0; } Someone can explain me why PHP serves more requests than CGI script?

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  • Performance issues when using SSD for a developer notebook (WAMP/LAMP stack)?

    - by András Szepesházi
    I'm a web application developer using my notebook as a standalone development environment (WAMP stack). I just switched from a Core2-duo Vista 32 bit notebook with 2Gb RAM and SATA HDD, to an i5-2520M Win7 64 bit with 4Gb RAM and 128 GB SDD (Corsair P3 128). My initial experience was what I expected, fast boot, quick load of all the applications (Eclipse takes now 5 seconds as opposed to 30s on my old notebook), overall great experience. Then I started to build up my development stack, both as LAMP (using VirtualBox with a debian guest) and WAMP (windows native apache + mysql + php). I wanted to compare those two. This still all worked great out, then I started to pull in my projects to these stacks. And here came the nasty surprise, one of those projects produced a lot worse response times than on my old notebook (that was true for both the VirtualBox and WAMP stack). Apache, php and mysql configurations were practically identical in all environments. I started to do a lot of benchmarking and profiling, and here is what I've found: All general benchmarks (Performance Test 7.0, HDTune Pro, wPrime2 and some more) gave a big advantage to the new notebook. Nothing surprising here. Disc specific tests showed that read/write operations peaked around 380M/160M for the SSD, and all the different sized block operations also performed very well. Started apache performance benchmarking with Apache Benchmark for a small static html file (10 concurrent threads, 500 iterations). Old notebook: min 47ms, median 111ms, max 156ms New WAMP stack: min 71ms, median 135ms, max 296ms New LAMP stack (in VirtualBox): min 6ms, median 46ms, max 175ms Right here I don't get why the native WAMP stack performed so bad, but at least the LAMP environment brought the expected speed. Apache performance measurement for non-cached php content. The php runs a loop of 1000 and generates sha1(uniqid()) inisde. Again, 10 concurrent threads, 500 iterations were used for the benchmark. Old notebook: min 0ms, median 39ms, max 218ms New WAMP stack: min 20ms, median 61ms, max 186ms New LAMP stack (in VirtualBox): min 124ms, median 704ms, max 2463ms What the hell? The new LAMP performed miserably, and even the new native WAMP was outperformed by the old notebook. php + mysql test. The test consists of connecting to a database and reading a single record form a table using INNER JOIN on 3 more (indexed) tables, repeated 100 times within a loop. Databases were identical. 10 concurrent threads, 100 iterations were used for the benchmark. Old notebook: min 1201ms, median 1734ms, max 3728ms New WAMP stack: min 367ms, median 675ms, max 1893ms New LAMP stack (in VirtualBox): min 1410ms, median 3659ms, max 5045ms And the same test with concurrency set to 1 (instead of 10): Old notebook: min 1201ms, median 1261ms, max 1357ms New WAMP stack: min 399ms, median 483ms, max 539ms New LAMP stack (in VirtualBox): min 285ms, median 348ms, max 444ms Strictly for my purposes, as I'm using a self contained development environment (= low concurrency) I could be satisfied with the second test's result. Though I have no idea why the VirtualBox environment performed so bad with higher concurrency. Finally I performed a test of including many php files. The application that I mentioned at the beginning, the one that was performing so bad, has a heavy bootstrap, loads hundreds of small library and configuration files while initializing. So this test does nothing else just includes about 100 files. Concurrency set to 1, 100 iterations: Old notebook: min 140ms, median 168ms, max 406ms New WAMP stack: min 434ms, median 488ms, max 604ms New LAMP stack (in VirtualBox): min 413ms, median 1040ms, max 1921ms Even if I consider that VirtualBox reached those files via shared folders, and that slows things down a bit, I still don't see how could the old notebook outperform so heavily both new configurations. And I think this is the real root of the slow performance, as the application uses even more includes, and the whole bootstrap will occur several times within a page request (for each ajax call, for example). To sum it up, here I am with a brand new high-performance notebook that loads the same page in 20 seconds, that my old notebook can do in 5-7 seconds. Needless to say, I'm not a very happy person right now. Why do you think I experience these poor performance values? What are my options to remedy this situation?

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  • Database Trends & Applications column: Database Benchmarking from A to Z

    - by KKline
    Have you heard of the monthly print and web magazine Database Trends & Applications (DBTA)? Did you know I'm the regular columnist covering SQL Server ? For the past six months, I've been writing a series of articles about database benchmarking culminating in the latest article discussing my three favorite database benchmarking tools: the free, open-source HammerDB, the native SQL Server Distributed Replay Utility, and the commercial Benchmark Factory from Dell / Quest Software. Wondering what...(read more)

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  • code metrics for .net code

    - by user20358
    While the code metrics tool gives a pretty good analysis of the code being analyzed, I was wondering if there was any such benchmark on acceptable standards for the following as well: Maximum number of types per assembly Maximum number of such types that can be accessible Maximum number of parameters per method Acceptable RFC count Acceptable Afferent coupling count Acceptable Efferent coupling count Any other metrics to judge the quality of .Net code by? Thanks for your time.

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  • Intel Xeon 5600 (Westmere-EP) and 7500 (Nehalem-EX)

    - by jchang
    Intel Xeon 5600 (Westmere-EP) and 7500 (Nehalem-EX) Performance Intel launched the Xeon 5600 series (Westmere-EP, 32nm) six-core processors on 16 March 2010 without any TPC benchmark results. In the performance world, no results almost always mean bad or not good results. Yet there is every reason to believe that the Xeon 5600 series with six-cores (X models only) will performance exactly as expected for a 50% increase in the number of cores at the same frequency (as the 5500) with no system level...(read more)

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  • MySQL Cluster 7.2: Over 8x Higher Performance than Cluster 7.1

    - by Mat Keep
    0 0 1 893 5092 Homework 42 11 5974 14.0 Normal 0 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin; mso-ansi-language:EN-US;} Summary The scalability enhancements delivered by extensions to multi-threaded data nodes enables MySQL Cluster 7.2 to deliver over 8x higher performance than the previous MySQL Cluster 7.1 release on a recent benchmark What’s New in MySQL Cluster 7.2 MySQL Cluster 7.2 was released as GA (Generally Available) in February 2012, delivering many enhancements to performance on complex queries, new NoSQL Key / Value API, cross-data center replication and ease-of-use. These enhancements are summarized in the Figure below, and detailed in the MySQL Cluster New Features whitepaper Figure 1: Next Generation Web Services, Cross Data Center Replication and Ease-of-Use Once of the key enhancements delivered in MySQL Cluster 7.2 is extensions made to the multi-threading processes of the data nodes. Multi-Threaded Data Node Extensions The MySQL Cluster 7.2 data node is now functionally divided into seven thread types: 1) Local Data Manager threads (ldm). Note – these are sometimes also called LQH threads. 2) Transaction Coordinator threads (tc) 3) Asynchronous Replication threads (rep) 4) Schema Management threads (main) 5) Network receiver threads (recv) 6) Network send threads (send) 7) IO threads Each of these thread types are discussed in more detail below. MySQL Cluster 7.2 increases the maximum number of LDM threads from 4 to 16. The LDM contains the actual data, which means that when using 16 threads the data is more heavily partitioned (this is automatic in MySQL Cluster). Each LDM thread maintains its own set of data partitions, index partitions and REDO log. The number of LDM partitions per data node is not dynamically configurable, but it is possible, however, to map more than one partition onto each LDM thread, providing flexibility in modifying the number of LDM threads. The TC domain stores the state of in-flight transactions. This means that every new transaction can easily be assigned to a new TC thread. Testing has shown that in most cases 1 TC thread per 2 LDM threads is sufficient, and in many cases even 1 TC thread per 4 LDM threads is also acceptable. Testing also demonstrated that in some instances where the workload needed to sustain very high update loads it is necessary to configure 3 to 4 TC threads per 4 LDM threads. In the previous MySQL Cluster 7.1 release, only one TC thread was available. This limit has been increased to 16 TC threads in MySQL Cluster 7.2. The TC domain also manages the Adaptive Query Localization functionality introduced in MySQL Cluster 7.2 that significantly enhanced complex query performance by pushing JOIN operations down to the data nodes. Asynchronous Replication was separated into its own thread with the release of MySQL Cluster 7.1, and has not been modified in the latest 7.2 release. To scale the number of TC threads, it was necessary to separate the Schema Management domain from the TC domain. The schema management thread has little load, so is implemented with a single thread. The Network receiver domain was bound to 1 thread in MySQL Cluster 7.1. With the increase of threads in MySQL Cluster 7.2 it is also necessary to increase the number of recv threads to 8. This enables each receive thread to service one or more sockets used to communicate with other nodes the Cluster. The Network send thread is a new thread type introduced in MySQL Cluster 7.2. Previously other threads handled the sending operations themselves, which can provide for lower latency. To achieve highest throughput however, it has been necessary to create dedicated send threads, of which 8 can be configured. It is still possible to configure MySQL Cluster 7.2 to a legacy mode that does not use any of the send threads – useful for those workloads that are most sensitive to latency. The IO Thread is the final thread type and there have been no changes to this domain in MySQL Cluster 7.2. Multiple IO threads were already available, which could be configured to either one thread per open file, or to a fixed number of IO threads that handle the IO traffic. Except when using compression on disk, the IO threads typically have a very light load. Benchmarking the Scalability Enhancements The scalability enhancements discussed above have made it possible to scale CPU usage of each data node to more than 5x of that possible in MySQL Cluster 7.1. In addition, a number of bottlenecks have been removed, making it possible to scale data node performance by even more than 5x. Figure 2: MySQL Cluster 7.2 Delivers 8.4x Higher Performance than 7.1 The flexAsynch benchmark was used to compare MySQL Cluster 7.2 performance to 7.1 across an 8-node Intel Xeon x5670-based cluster of dual socket commodity servers (6 cores each). As the results demonstrate, MySQL Cluster 7.2 delivers over 8x higher performance per data nodes than MySQL Cluster 7.1. More details of this and other benchmarks will be published in a new whitepaper – coming soon, so stay tuned! In a following blog post, I’ll provide recommendations on optimum thread configurations for different types of server processor. You can also learn more from the Best Practices Guide to Optimizing Performance of MySQL Cluster Conclusion MySQL Cluster has achieved a range of impressive benchmark results, and set in context with the previous 7.1 release, is able to deliver over 8x higher performance per node. As a result, the multi-threaded data node extensions not only serve to increase performance of MySQL Cluster, they also enable users to achieve significantly improved levels of utilization from current and future generations of massively multi-core, multi-thread processor designs.

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  • Fastest Functional Language

    - by Farouk
    I've recently been delving into functional programming especially Haskell and F#, the prior more so. After some googling around I could not find a benchmark comparison of the more prominent functional languages (Scala,F# etc). I know it's not necessarily fair to some of the languages (Scala comes to mind) given that they are hybrids, but I just wanna know which outperforms which on what operations and overall.

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  • Excellent Windows Azure benchmarks

    - by Sarang
    The Extreme computing group has released a fairly comprehensive set of benchmarks  for almost all aspects of WA. They have also provided the source code to alleviate all doubts that may surface with the MSFT logo lurking around the top right of their homepage :) (Which also resides at a cloudapp.net url). The code is simple and the tests comprehensive enough to hold as data points for customer interactions. Add to it the clean no nonsense Silverlight charts to render the benchmarks and you are set to sell. Technorati Tags: Azure,Benchmark,Extreme Computing Group

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  • Intel Xeon E5 (Sandy Bridge-EP) and SQL Server 2012 Benchmarks

    - by jchang
    Intel officially announced the Xeon E5 2600 series processor based on Sandy Bridge-EP variant with upto 8 cores and 20MB LLC per socket. Only one TPC benchmark accompanied product launch, summary below. Processors Cores per Frequency Memory SQL Vendor TPC-E 2 x Xeon E5-2690 8 2.9GHz 512GB (16x32GB) 2012 IBM 1,863.23 2 x Xeon E7-2870 10 2.4GHz 512GB (32x16GB) 2008R2 IBM 1,560.70 2 x Xeon X5690 6 3.46GHz 192GB (12x16GB) 2008R2 HP 1,284.14 Note: the HP report lists SQL Server 2008 R2 Enterprise Edition...(read more)

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  • Find a Faster DNS Server with Namebench

    - by Mysticgeek
    One way to speed up your Internet browsing experience is using a faster DNS server. Today we take a look at Namebench, which will compare your current DNS server against others out there, and help you find a faster one. Namebench Download the file and run the executable (link below). Namebench starts up and will include the current DNS server you have configured on your system. In this example we’re behind a router and using the DNS server from the ISP. Include the global DNS providers and the best available regional DNS server, then start the Benchmark. The test starts to run and you’ll see the queries it’s running through. The benchmark takes about 5-10 minutes to complete. After it’s complete you’ll get a report of the results. Based on its findings, it will show you what DNS server is fastest for your system. It also displays different types of graphs so you can get a better feel for the different results. You can export the results to a .csv file as well so you can present the results in Excel. Conclusion This is a free project that is in continuing development, so results might not be perfect, and there may be more features added in the future. If you’re looking for a method to help find a faster DNS server for your system, Namebench is a cool free utility to help you out. If you’re looking for a public DNS server that is customizable and includes filters, you might want to check out our article on helping to protect your kids from questionable content using OpenDNS. You can also check out how to speed up your web browsing with Google Public DNS. Links Download NameBench for Windows, Mac, and Linux from Google Code Learn More About the Project on the Namebench Wiki Page Similar Articles Productive Geek Tips Open a Second Console Session on Ubuntu ServerShare Ubuntu Home Directories using SambaSetup OpenSSH Server on Ubuntu LinuxDisable the Annoying “This device can perform faster” Balloon Message in Windows 7Search For Rows With Special Characters in SQL Server TouchFreeze Alternative in AutoHotkey The Icy Undertow Desktop Windows Home Server – Backup to LAN The Clear & Clean Desktop Use This Bookmarklet to Easily Get Albums Use AutoHotkey to Assign a Hotkey to a Specific Window Latest Software Reviews Tinyhacker Random Tips DVDFab 6 Revo Uninstaller Pro Registry Mechanic 9 for Windows PC Tools Internet Security Suite 2010 How to Add Exceptions to the Windows Firewall Office 2010 reviewed in depth by Ed Bott FoxClocks adds World Times in your Statusbar (Firefox) Have Fun Editing Photo Editing with Citrify Outlook Connector Upgrade Error Gadfly is a cool Twitter/Silverlight app

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  • Das T5-4 TPC-H Ergebnis naeher betrachtet

    - by Stefan Hinker
    Inzwischen haben vermutlich viele das neue TPC-H Ergebnis der SPARC T5-4 gesehen, das am 7. Juni bei der TPC eingereicht wurde.  Die wesentlichen Punkte dieses Benchmarks wurden wie gewohnt bereits von unserer Benchmark-Truppe auf  "BestPerf" zusammengefasst.  Es gibt aber noch einiges mehr, das eine naehere Betrachtung lohnt. Skalierbarkeit Das TPC raet von einem Vergleich von TPC-H Ergebnissen in unterschiedlichen Groessenklassen ab.  Aber auch innerhalb der 3000GB-Klasse ist es interessant: SPARC T4-4 mit 4 CPUs (32 Cores mit 3.0 GHz) liefert 205,792 QphH. SPARC T5-4 mit 4 CPUs (64 Cores mit 3.6 GHz) liefert 409,721 QphH. Das heisst, es fehlen lediglich 1863 QphH oder 0.45% zu 100% Skalierbarkeit, wenn man davon ausgeht, dass die doppelte Anzahl Kerne das doppelte Ergebnis liefern sollte.  Etwas anspruchsvoller, koennte man natuerlich auch einen Faktor von 2.4 erwarten, wenn man die hoehere Taktrate mit beruecksichtigt.  Das wuerde die Latte auf 493901 QphH legen.  Dann waere die SPARC T5-4 bei 83%.  Damit stellt sich die Frage: Was hat hier nicht skaliert?  Vermutlich der Plattenspeicher!  Auch hier lohnt sich eine naehere Betrachtung: Plattenspeicher Im Bericht auf BestPerf und auch im Full Disclosure Report der TPC stehen einige interessante Details zum Plattenspeicher und der Konfiguration.   In der Konfiguration der SPARC T4-4 wurden 12 2540-M2 Arrays verwendet, die jeweils ca. 1.5 GB/s Durchsatz liefert, insgesamt also eta 18 GB/s.  Dabei waren die Arrays offensichtlich mit jeweils 2 Kabeln pro Array direkt an die 24 8GBit FC-Ports des Servers angeschlossen.  Mit den 2x 8GBit Ports pro Array koennte man so ein theoretisches Maximum von 2GB/s erreichen.  Tatsaechlich wurden 1.5GB/s geliefert, was so ziemlich dem realistischen Maximum entsprechen duerfte. Fuer den Lauf mit der SPARC T5-4 wurden doppelt so viele Platten verwendet.  Dafuer wurden die 2540-M2 Arrays mit je einem zusaetzlichen Plattentray erweitert.  Mit dieser Konfiguration wurde dann (laut BestPerf) ein Maximaldurchsatz von 33 GB/s erreicht - nicht ganz das doppelte des SPARC T4-4 Laufs.  Um tatsaechlich den doppelten Durchsatz (36 GB/s) zu liefern, haette jedes der 12 Arrays 3 GB/s ueber seine 4 8GBit Ports liefern muessen.  Im FDR stehen nur 12 dual-port FC HBAs, was die Verwendung der Brocade FC Switches erklaert: Es wurden alle 4 8GBit ports jedes Arrays an die Switches angeschlossen, die die Datenstroeme dann in die 24 16GBit HBA ports des Servers buendelten.  Das theoretische Maximum jedes Storage-Arrays waere nun 4 GB/s.  Wenn man jedoch den Protokoll- und "Realitaets"-Overhead mit einrechnet, sind die tatsaechlich gelieferten 2.75 GB/s gar nicht schlecht.  Mit diesen Zahlen im Hinterkopf ist die Verdopplung des SPARC T4-4 Ergebnisses eine gute Leistung - und gleichzeitig eine gute Erklaerung, warum nicht bis zum 2.4-fachen skaliert wurde. Nebenbei bemerkt: Weder die SPARC T4-4 noch die SPARC T5-4 hatten in der gemessenen Konfiguration irgendwelche Flash-Devices. Mitbewerb Seit die T4 Systeme auf dem Markt sind, bemuehen sich unsere Mitbewerber redlich darum, ueberall den Eindruck zu hinterlassen, die Leistung des SPARC CPU-Kerns waere weiterhin mangelhaft.  Auch scheinen sie ueberzeugt zu sein, dass (ueber)grosse Caches und hohe Taktraten die einzigen Schluessel zu echter Server Performance seien.  Wenn ich mir nun jedoch die oeffentlichen TPC-H Ergebnisse ansehe, sehe ich dies: TPC-H @3000GB, Non-Clustered Systems System QphH SPARC T5-4 3.6 GHz SPARC T5 4/64 – 2048 GB 409,721.8 SPARC T4-4 3.0 GHz SPARC T4 4/32 – 1024 GB 205,792.0 IBM Power 780 4.1 GHz POWER7 8/32 – 1024 GB 192,001.1 HP ProLiant DL980 G7 2.27 GHz Intel Xeon X7560 8/64 – 512 GB 162,601.7 Kurz zusammengefasst: Mit 32 Kernen (mit 3 GHz und 4MB L3 Cache), liefert die SPARC T4-4 mehr QphH@3000GB ab als IBM mit ihrer 32 Kern Power7 (bei 4.1 GHz und 32MB L3 Cache) und auch mehr als HP mit einem 64 Kern Intel Xeon System (2.27 GHz und 24MB L3 Cache).  Ich frage mich, wo genau SPARC hier mangelhaft ist? Nun koennte man natuerlich argumentieren, dass beide Ergebnisse nicht gerade neu sind.  Nun, in Ermangelung neuerer Ergebnisse kann man ja mal ein wenig spekulieren: IBMs aktueller Performance Report listet die o.g. IBM Power 780 mit einem rPerf Wert von 425.5.  Ein passendes Nachfolgesystem mit Power7+ CPUs waere die Power 780+ mit 64 Kernen, verfuegbar mit 3.72 GHz.  Sie wird mit einem rPerf Wert von  690.1 angegeben, also 1.62x mehr.  Wenn man also annimmt, dass Plattenspeicher nicht der limitierende Faktor ist (IBM hat mit 177 SSDs getestet, sie duerfen das gerne auf 400 erhoehen) und IBMs eigene Leistungsabschaetzung zugrunde legt, darf man ein theoretisches Ergebnis von 311398 QphH@3000GB erwarten.  Das waere dann allerdings immer noch weit von dem Ergebnis der SPARC T5-4 entfernt, und gerade in der von IBM so geschaetzen "per core" Metric noch weniger vorteilhaft. In der x86-Welt sieht es nicht besser aus.  Leider gibt es von Intel keine so praktischen rPerf-Tabellen.  Daher muss ich hier fuer eine Schaetzung auf SPECint_rate2006 zurueckgreifen.  (Ich bin kein grosser Fan von solchen Kreuz- und Querschaetzungen.  Insb. SPECcpu ist nicht besonders geeignet, um Datenbank-Leistung abzuschaetzen, da fast kein IO im Spiel ist.)  Das o.g. HP System wird bei SPEC mit 1580 CINT2006_rate gelistet.  Das bis einschl. 2013-06-14 beste Resultat fuer den neuen Intel Xeon E7-4870 mit 8 CPUs ist 2180 CINT2006_rate.  Das ist immerhin 1.38x besser.  (Wenn man nur die Taktrate beruecksichtigen wuerde, waere man bei 1.32x.)  Hier weiter zu rechnen, ist muessig, aber fuer die ungeduldigen Leser hier eine kleine tabellarische Zusammenfassung: TPC-H @3000GB Performance Spekulationen System QphH* Verbesserung gegenueber der frueheren Generation SPARC T4-4 32 cores SPARC T4 205,792 2x SPARC T5-464 cores SPARC T5 409,721 IBM Power 780 32 cores Power7 192,001 1.62x IBM Power 780+ 64 cores Power7+  311,398* HP ProLiant DL980 G764 cores Intel Xeon X7560 162,601 1.38x HP ProLiant DL980 G780 cores Intel Xeon E7-4870    224,348* * Keine echten Resultate  - spekulative Werte auf der Grundlage von rPerf (Power7+) oder SPECint_rate2006 (HP) Natuerlich sind IBM oder HP herzlich eingeladen, diese Werte zu widerlegen.  Aber stand heute warte ich noch auf aktuelle Benchmark Veroffentlichungen in diesem Datensegment. Was koennen wir also zusammenfassen? Es gibt einige Hinweise, dass der Plattenspeicher der begrenzende Faktor war, der die SPARC T5-4 daran hinderte, auf jenseits von 2x zu skalieren Der Mythos, dass SPARC Kerne keine Leistung bringen, ist genau das - ein Mythos.  Wie sieht es umgekehrt eigentlich mit einem TPC-H Ergebnis fuer die Power7+ aus? Cache ist nicht der magische Performance-Schalter, fuer den ihn manche Leute offenbar halten. Ein System, eine CPU-Architektur und ein Betriebsystem jenseits einer gewissen Grenze zu skalieren ist schwer.  In der x86-Welt scheint es noch ein wenig schwerer zu sein. Was fehlt?  Nun, das Thema Preis/Leistung ueberlasse ich gerne den Verkaeufern ;-) Und zu guter Letzt: Nein, ich habe mich nicht ins Marketing versetzen lassen.  Aber manchmal kann ich mich einfach nicht zurueckhalten... Disclosure Statements The views expressed on this blog are my own and do not necessarily reflect the views of Oracle. TPC-H, QphH, $/QphH are trademarks of Transaction Processing Performance Council (TPC). For more information, see www.tpc.org, results as of 6/7/13. Prices are in USD. SPARC T5-4 409,721.8 QphH@3000GB, $3.94/QphH@3000GB, available 9/24/13, 4 processors, 64 cores, 512 threads; SPARC T4-4 205,792.0 QphH@3000GB, $4.10/QphH@3000GB, available 5/31/12, 4 processors, 32 cores, 256 threads; IBM Power 780 QphH@3000GB, 192,001.1 QphH@3000GB, $6.37/QphH@3000GB, available 11/30/11, 8 processors, 32 cores, 128 threads; HP ProLiant DL980 G7 162,601.7 QphH@3000GB, $2.68/QphH@3000GB available 10/13/10, 8 processors, 64 cores, 128 threads. SPEC and the benchmark names SPECfp and SPECint are registered trademarks of the Standard Performance Evaluation Corporation. Results as of June 18, 2013 from www.spec.org. HP ProLiant DL980 G7 (2.27 GHz, Intel Xeon X7560): 1580 SPECint_rate2006; HP ProLiant DL980 G7 (2.4 GHz, Intel Xeon E7-4870): 2180 SPECint_rate2006,

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