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.
“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.”
Dr. Amit Seti from IIT-Gauwhati presented this talk at GTCx in India. “This talk will cover how medical imaging data can be used to train computer vision systems that automate diagnostic analysis in current clinical practice. Not only that, with more creative use of data, we can go even beyond that to predict outcome of specific treatment for individual patients. We will cover results from prostate and breast cancers to show that a future is not too far where algorithms will become a necessary set of tools in a pathologist’s toolbox.”
“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.”
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.
A survey conducted by insideHPC and Gabriel Consulting in Q4 of 2105 indicated that nearly 45% of HPC and large enterprise customers would spend more on system interconnects and I/O in 2016, with 40% maintaining spending at the same level as the prior year. For manufacturing, the largest subset representing approximately one third of the respondents, over 60% were planning to spend more and almost 30% maintaining the same level of spending going into 2016 implying the critical value of high performance interconnects.
As data center sprawl is now understood to be expensive and may not deliver performance increases for all types of applications, new technologies are coming to the rescue. A field-programmable gate array (FPGA) is an integrated circuit designed to be configured by a customer or a designer after manufacturing – hence “field-programmable”. While the use of GPUs and HPC accelerators are generally understood today, there are a number of misconceptions about FPGAs that need to be understood.
FPGAs will become increasing important for organizations that have a wide range of applications that can benefit from performance increases. Rather than a brute force method to increasing performance in a data center by purchasing and maintaining racks of hardware and associated costs, FPGAs may be able to equal and exceed the performance of additional servers, while reducing costs as well.
As exponential data growth reshapes the industry, engineering, and scientific discovery, success has come to depend on the ability to analyze and extract insight from incredibly large data sets. Exascale computing will allow us to process data, run systems, and solve problems at a totally new scale and this will become vitally important as problems grow ever larger, ever more difficult. Our unmatched ability to bring new technology to the mainstream will provide systems that are markedly more affordable, usable, and efficient at handling growing workloads. To learn more download this white paper.
Organizations that implement high-performance computing (HPC) technologies have a wide range of requirements. From small manufacturing suppliers to national research institutions, using significant computing technologies is critical to creating innovative products and leading-edge research. No two HPC installations are the same. For maximum return, budget, software requirements, performance and customization all must be considered before installing and operating a successful environment. To learn more down load this white paper.