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Structured Parallel Programming
作者 Michael McCool、Arch D. Robison 和 James Reinders 采用一种基于结构性形式的途径,从而使该课题能为每一位软件开发人员所接受。

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Intel Academic Community at Argentina: Riding the Waves
作者:Ricardo Medel (Intel)张贴日期:04/26/20112
Our lively Intel Academic Community is helping Argentine universities to improve the teaching of parallel programming topics.
How to make fewer errors at the stage of code writing. Part N2.
作者:Andrey Karpov张贴日期:04/22/20110
This is the second article on avoiding certain errors at the early stage of code writing.
VTU and Intel Announce Composite Multicore Curriculum Revision
作者:Lauren Dankiewicz (Intel)张贴日期:04/20/20110
• VTU to implement a revised curriculum across all 8 semesters around Multicore for undergraduate engineering students • Parallel programming developed in association with IISc and VTU, with consultancy from Intel
Hadoop and HBase Optimization for Read Intensive Search Applications
作者:Abinasha Karana张贴日期:04/19/20112
by Abinasha Karana, DirectorBizosys Technologies Pvt Ltd, Bangalore Abstract Traditionally, web scale is achieved by master-slave replication and data sharing, which can be a huge challenge as data scales beyond 500Gb. Map-Reduce based technologies such as HBase* and Hadoop* achieve web scale a...
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Using thread_local on C++ throws error
作者:Rihab A.5
I have been trying to convert a C++ MPI code into OpenMP. There are large number of static member variables (mostly dynamic lists of class objects), and i am trying to use 'thread_local' to make sure there are no conflicts. But the file does not compile and threw error: "error: expected a ";"". I was using ICC 14.  When i tried to use ICC 15 beta version, the particular file where i used thread_local compiled, but the compilation of the whole application failed at some other point: "undefined reference to '__cxa_thread_atexit'". Would greatly appreciate help in solving this issue.  
Poor threading performance on Intel Xeon E5-2680 v2
作者:Pascal10
Hello I am running a visualization program (visualizing a large dataset) where I can either use MPI or pthreads. When I run it on my desktop which has an Intel i7-2600K (4 cores, 8 threads), I get better performance using pThreads (I'm using a lot of threads, e.g 32) compared to using MPI which is normal (I guess). But when I run the same code on one node (which is part of a cluster) which has Intels Xeon E5-2680 v2 (10 cores, 20 threads), the performance I get using pthreads is worse than MPI; about 70s while using MPI compared to 180s using pthreads. Even worse, the performance on the Intel Xeon E5-2680 v2 is lower than on that of the Intel i7-2600K, it's around 100s on the 2600k but 180 on the  E5-2680 (same number of threads on both). I check using the top command and all the cores are active when I run the program.   So my question is why is that happening? Is there some other way I should be compiling the code on the E5-2680? Is there some variables I should set like KMP_AFFIN...
HTM/STM and Scheduling
作者:Simone A.1
Hi, I have a question about Hardware and Software Transactional Memory. Given the types of versioning (eager and lazy) and conflict detection (optimistic and pessimistic) and let's say that 2 or more threads are performing a transaction that write/read the same memory location. The scheduling of the threads could affect the ability of detect a conflict? Which combination of versioning and conflict detection would be better to always catch the conflicts? Hope my question is clear. Thanks. Best Regards, Simone
Locking CPU cache lines for a thread ( L1)
作者:Younis A.14
Hi I'm working on securing access to L1 cache by locking it line by line. Is there any way to do it? For example, two threads accessing the L1 and L1 lines are locked for a certain time to each thread accessed them. Regards, Younis
Responsive OpenMP Theads in Hybrid Parallel Environment
作者:Don K.1
I have a Fortran code that runs both MPI and OpenMP.  I have done some profiling of the code on an 8 core windows laptop varying the number of mpi  tasks vs. openmp threads and have some understanding of where some performance bottlenecks for each parallel method might surface.  The problem I am having is when I port over to a Linux cluster with several 8-core nodes.  Specifically, my openmp thread parallelism performance is very poor.  Running 8 mpi tasks per node is significantly faster than 8 openmp threads per node (1 mpi task), but even 2 omp threads + 4 mpi tasks runs was running very slowly, more so than I could solely attribute to a thread starvation issue.  I saw a few related posts in this area and am hoping for further insight and recommendations in to this issue.  What I have tried so far ... 1.  setenv OMP_WAIT_POLICY active      ## seems to make sense 2.  setenv KMP_BLOCKTIME 1          ## this is counter to what I have read but when I set this to a large number (2500...
Optimizing cilk with ternary conditional
作者:Fabio G.3
What is the best way to optimize the cycle cilk_for(i=0;i<n;i++){ x[i]=x[i]<0?0:x[i]; }or somethings like that? Thanks, Fabio
have asked them to
作者:Robert P.0
ICC t20 World Cup 2014 Live StreamIndia vs Pakistan Live Stream
Optimizing reduce_by_key implementation using TBB
作者:Shruti R.0
Hello Everyone, I'm quite new to TBB & have been trying to optimize reduce_by_key implementation using TBB constructs. However serial STL code is always outperforming the TBB code! It would be helpful if I'm given an idea about how reduce_by_key can be improvised using tbb::parallel_scan. Any help at the earliest would be much appreciated. Thanks.
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