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Deep Learning Frameworks Get a Performance Benefit from Intel MKL Matrix-Matrix Multiplication

Intel® Math Kernel Library 2017 (Intel® MKL 2017) includes new GEMM kernels that are optimized for various skewed matrix sizes. The new kernels take advantage of Intel® Advanced Vector Extensions 512 (Intel® AVX-512) and achieves high GEMM performance on multicore and many-core Intel® architectures, particularly for situations arising from deep neural networks..

Multicore Performance Challenges for Game Developers

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 OpenMP API Celebrates 20 Years of Success

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.

C++ Parallel STL Introduced in Intel Parallel Studio XE 2018 Beta

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

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

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

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

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

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

“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.”