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.
Intel Advisor Roofline Analysis Finds New Opportunities for Optimizing Application Performance
April 27, 2017 by
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.
Intel® VTune™ Amplifier Turns Raw Profiling Data Into Performance Insights
April 13, 2017 by
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.
Intel MKL and Intel TBB Working Together for Performance
March 23, 2017 by
When used in a TBB environment, Intel has demonstrated a many-fold performance improvement over the same parallelized code using Intel MKL in an OpenMP environment. Intel TBB-enabled Intel MKL is ideal when there is heavy threading in the Intel TBB application. Intel TBB-enabled Intel MKL shows solid performance improvements through better interoperability with other parts of the workload.
Intel MPI Library 2017 Focuses on Intel Multi-core/Many-Core Clusters
March 9, 2017 by
With the release of Intel Parallel Studio XE 2017, the focus is on making applications perform better on Intel architecture-based clusters. Intel MPI Library 2017, a fully integrated component of Intel Parallel Studio XE 2017, implements the high-performance MPI-3.1 specification on multiple fabrics. It enables programmers to quickly deliver the best parallel performance, even if you change or upgrade to new interconnects, without requiring changes to the software or operating environment.
Intel DAAL Accelerates Data Analytics and Machine Learning
February 23, 2017 by
Intel DAAL is a high-performance library specifically optimized for big data analysis on the latest Intel platforms, including Intel Xeon®, and Intel Xeon Phi™. It provides the algorithmic building blocks for all stages in data analysis in offline, batch, streaming, and distributed processing environments. It was designed for efficient use over all the popular data platforms and APIs in use today, including MPI, Hadoop, Spark, R, MATLAB, Python, C++, and Java.
Intel Releases Optimized Python for HPC
February 9, 2017 by
“By implementing popular Python packages such as NumPy, SciPy, scikit-learn, to call the Intel Math Kernel Library (Intel MKL) and the Intel Data Analytics Acceleration Library (Intel DAAL), Python applications are automatically optimized to take advantage of the latest architectures. These libraries have also been optimized for multithreading through calls to the Intel Threading Building Blocks (Intel TBB) library. This means that existing Python applications will perform significantly better merely by switching to the Intel distribution.”
Achieving High-Performance Math Processing with Intel MKL 2017
January 26, 2017 by
“Many of the libraries developed in the 70s and 80s for core linear algebra and scientific math computation, such as BLAS, LAPACK, FFT, are still in use today with C, C++, Fortran, and even Python programs. With MKL, Intel has engineered a ready-to-use, royalty-free library that implements these numerical algorithms optimized specifically to take advantage of the latest features of Intel chip architectures. Even the best compiler can’t compete with the level of performance possible from a hand-optimized library. Any application that already relies on the BLAS or LAPACK functionality will achieve better performance on Intel and compatible architectures just by downloading and re-linking with Intel MKL.”
A Decade of Multicore Parallelism with Intel TBB
January 12, 2017 by
While HPC developers worry about squeezing out the ultimate performance while running an application on dedicated cores, Intel TBB tackles a problem that HPC users never worry about: How can you make parallelism work well when you share the cores that you run upon?” This is more of a concern if you’re running that application on a many-core laptop or workstation than a dedicated supercomputer because who knows what will also be running on those shared cores. Intel Threaded Building Blocks reduce the delays from other applications by utilizing a revolutionary task-stealing scheduler. This is the real magic of TBB.
