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Spack – A Package Manager for HPC

Todd Gamblin from LLNL gave this talk at the Stanford HPC Conference. “Spack is a package manager for cluster users, developers and administrators. Rapidly gaining popularity in the HPC community, like other HPC package managers, Spack was designed to build packages from source. This talk will introduce some of the open infrastructure for distributing packages, challenges to providing binaries for a large package ecosystem and what we’re doing to address problems.”

Call for Submissions: OpenMPCon and IWOMP 2019 in New Zealand

The OpenMP community has issued its Call for Submissions for OpenMPCon 2019 and IWOMP 2019. The events take place September 9-13 in Auckland, New Zealand. “OpenMPCon is the annual conference for OpenMP developers to discuss of all aspects of parallel programming with OpenMP. The International Workshop on OpenMP (IWOMP) is an annual workshop dedicated to the promotion and advancement of all aspects of parallel programming with OpenMP.”

Intel High-Performance Python Extends to Machine Learning and Data Analytics

One of the big surprises of the past few years has been the spectacular rise in the use of Python* in high-performance computing applications. With the latest releases of Intel® Distribution for Python, included in Intel® Parallel Studio XE 2019, the numerical and scientific computing capabilities of high-performance Python now extends to machine learning and data analytics.

NERSC Hosts GPU Hackathon in Preparation for Perlmutter Supercomputer

NERSC recently hosted a successful GPU Hackathon event in preparation for their next-generation Perlmutter supercomputer. Perlmutter, a pre-exascale Cray Shasta system slated to be delivered in 2020, will feature a number of new hardware and software innovations and is the first supercomputing system designed with both data analysis and simulations in mind. Unlike previous NERSC systems, Perlmutter will use a combination of nodes with only CPUs, as well as nodes featuring both CPUs and GPUs.

Improving HPC Performance with the Roofline Model

“When we are optimizing our objective is to determine which hardware resource the code is exhausting (there must be one, otherwise it would run faster!), and then see how to modify the code to reduce its need for that resource. It is therefore essential to understand the maximum theoretical performance of that aspect of the machine, since if we are already achieving the peak performance we should give up, or choose a different algorithm.”

Python Power: Intel SDK Accelerates Python Development and Execution

It was with one goal – accelerating Python execution performance – that lead to the creation of Intel Distribution for Python, a set of tools designed to provide Python application performance right out of the box, usually with no code changes required. This sponsored post from Intel highlights how Intel SDK can enhance Python development and execution, as Python continues to grow in popularity.

Putting Computer Vision to Work with OpenVINO

OpenVINO is a single toolkit, optimized for Intel hardware, that the data scientist and AI software developer can use for quickly developing high-performance applications that employ neural network inference and deep learning to emulate human vision over various platforms. “This toolkit supports heterogeneous execution across CPUs and computer vision accelerators including GPUs, Intel® Movidius™ hardware, and FPGAs.”

NVIDIA steps up with Nsight Systems Performance Analysis Tool

Today NVIDIA announced that NVIDIA Nsight Systems 2019.1 is now available for download. As a system-wide performance analysis tool. With it, developers can visualize application algorithms, identify large optimization opportunities, and tune/scale efficiently across CPUs and GPUs. “In this release, we introduce a wide range of new features, refinements, and fixes. The enhancements aim to improve a user’s ability to analyze neural network performance, locate graphical stutter, and increase pattern discoverability.”

Video: Speeding up Programs with OpenACC in GCC

Thomas Schwinge from Mentor gave this talk at FOSDEM’19. “Requiring only few changes to your existing source code, OpenACC allows for easy parallelization and code offloading to accelerators such as GPUs. We will present a short introduction of GCC and OpenACC, implementation status, examples, and performance results.”

Argonne Looks to Singularity for HPC Code Portability

Over at Argonne, Nils Heinonen writes that Researchers are using the open source Singularity framework as a kind of Rosetta Stone for running supercomputing code almost anywhere. “Once a containerized workflow is defined, its image can be snapshotted, archived, and preserved for future use. The snapshot itself represents a boon for scientific provenance by detailing the exact conditions under which given data were generated: in theory, by providing the machine, the software stack, and the parameters, one’s work can be completely reproduced.”