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Articles on this Page
- 05/21/15--06:38: _Direct N-Body Simul...
- 02/18/16--07:02: _OpenMP and OpenCL o...
- 08/31/16--18:52: _Design Optimization...
- 04/13/17--00:06: _Intel® VTune™ Ampli...
- 04/27/17--07:00: _Intel Advisor Roofl...
- 05/11/17--07:51: _C++ Parallel STL In...
- 05/25/17--07:55: _The OpenMP API Cele...
- 06/08/17--06:00: _Multicore Performan...
- 09/28/17--07:07: _OpenMP at 20 Moving...
- 11/09/17--07:15: _Building Fast Data ...
- 05/21/15--06:38: Direct N-Body Simulation
- 02/18/16--07:02: OpenMP and OpenCL on Intel Xeon Phi
- 08/31/16--18:52: Design Optimization for HPC Clusters
- 05/11/17--07:51: C++ Parallel STL Introduced in Intel Parallel Studio XE 2018 Beta
- 05/25/17--07:55: The OpenMP API Celebrates 20 Years of Success
- 06/08/17--06:00: Multicore Performance Challenges for Game Developers
- 09/28/17--07:07: OpenMP at 20 Moving Forward to 5.0
In some domains, an N-Body simulation is key to solving for the movement and forces of a dynamic system of particles. At each time step, the force that one body exacts on each other, and then the velocity can be computed. The simulation can continue up to a desired number of time steps.
"In a heterogeneous system that combines both the Intel Xeon CPU and the Intel Xeon Phi coprocessor, there are various options available to optimize applications. Whether one has an advantage over another is somewhat dependent on the application that is being run. Comparisons can be made comparing the two methods, as long as the algorithm lends itself to run and take advantage of either OpenMP or OpenCL."
Advanced simulation software can dramatically shorten the design phase by allowing engineers to virtually optimize and validate new ideas earlier in the process, minimizing the expense of building physical prototypes and streamlining real-world testing.
Discovering where the performance bottlenecks are and knowing what to do about it can be a mysterious and complex art, needing some very sophisticated performance analysis tools for success. That’s where Intel® VTune™ Amplifier XE 2017, part of Intel Parallel Studio XE, comes in.
The post Intel® VTune™ Amplifier Turns Raw Profiling Data Into Performance Insights appeared first on insideHPC.
Intel Advisor, an integral part of Intel Parallel Studio XE 2017, can help identify portions of code that could be good candidates for parallelization (both vectorization and threading). It can also help determine when it might not be appropriate to parallelize a section of code, depending on the platform, processor, and configuration it’s running on. Intel Advisor Roofline Analysis reveals the gap between an application’s performance and its expected performance.
The post Intel Advisor Roofline Analysis Finds New Opportunities for Optimizing Application Performance appeared first on insideHPC.
Parallel STL now makes it possible to transform existing sequential C++ code to take advantage of the threading and vectorization capabilities of modern hardware architectures. It does this by extending the C++ Standard Template Library with an execution policy argument that specifies the degree of threading and vectorization for each algorithm used.
The post C++ Parallel STL Introduced in Intel Parallel Studio XE 2018 Beta appeared first on insideHPC.
OpenMP is a good example of how hardware and software vendors, researchers, and academia, volunteering to work together, can successfully design a standard that benefits the entire developer community. Today, most software vendors track OpenMP advances closely and have implemented the latest API features in their compilers and tools. With OpenMP, application portability is assured across the latest multicore systems, including Intel Xeon Phi processors.
Game developers face a unique challenge – how to make their graphics-heavy applications perform well across a very wide spectrum of hardware devices, not just high-end systems. So while an early version of a game might have been developed on some high-end system with 10 teraflops of CPU potential in a discrete graphics card, how do you scale it down to smaller consumer devices where optimization options are more limited?
The post Multicore Performance Challenges for Game Developers appeared first on insideHPC.
This year, OpenMP*, the widely used API for shared memory parallelism supported in many C/C++ and Fortran compilers, turns 20. OpenMP is a great example of how hardware and software vendors, researchers, and academia, volunteering to work together, can successfully design a specification that benefits the entire developer community.
Intel® Integrated Performance Primitives (Intel IPP) is a highly optimized, production-ready, library for lossless data compression/decompression targeting image, signal, and data processing, and cryptography applications. Intel IPP includes more than 2,500 image processing, 1,300 signal processing, 500 computer vision, and 300 cryptography optimized functions for creating digital media, enterprise data, embedded, communications, and scientific, technical, and security applications.
The post Building Fast Data Compression Code with Intel Integrated Performance Primitives (Intel IPP) 2018 appeared first on insideHPC.