Getting ready for KNL? Take a Lesson from NERSC on Optimizing

February 10, 2016

NERSC is beginning to tell the world how to optimize applications to run on the new Intel Xeon Phi processors, code name Knights Landing (KNL), that will boot i Read more…

Contrary View: CPUs Sometimes Best for Big Data Visualization

December 1, 2015

Contrary to conventional thinking, GPUs are often not the best vehicles for big data visualization. In this commentary, I discuss several key technical reasons Read more…

Speaking Many Languages into the MIC

May 8, 2013

Traditional HPC languages, Fortran, C and C++, have little native control over hardware capabilities such as SIMD operations, multi-core availability and prefetch instructions. The burden of optimization is therefore... Read more…

Heterogeneous Computing in Firing Range

April 8, 2013

Despite developer hassle, this is a great problem from the perspective of companies who are finding ways to tailor clean layers around complex code for heterogeneous computing. Take, for example, Atlanta-based AccelerEyes, which is seeing booming business because of the demand for GPU acceleration and interest in kicking the Xeon Phi co-processor tires. Read more…

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Whitepaper

Porting CUDA Applications to Run on AMD GPUs

Giving developers the ability to write code once and use it on different platforms is important. Organizations are increasingly moving to open source and open standard solutions which can aid in code portability. AMD developed a porting solution that allows developers to port proprietary NVIDIA® CUDA® code to run on AMD graphic processing units (GPUs).

This paper describes the AMD ROCm™ open software platform which provides porting tools to convert NVIDIA CUDA code to AMD native open-source Heterogeneous Computing Interface for Portability (HIP) that can run on AMD Instinct™ accelerator hardware. The AMD solution addresses performance and portability needs of artificial intelligence (AI), machine learning (ML) and high performance computing (HPC) for application developers. Using the AMD ROCm platform, developers can port their GPU applications to run on AMD Instinct accelerators with very minimal changes to be able to run their code in both NVIDIA and AMD environments.

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Sponsored by AMD

Whitepaper

QCT HPC BeeGFS Storage: A Performance Environment for I/O Intensive Workloads

A workload-driven system capable of running HPC/AI workloads is more important than ever. Organizations face many challenges when building a system capable of running HPC and AI workloads. There are also many complexities in system design and integration. Building a workload driven solution requires expertise and domain knowledge that organizational staff may not possess.

This paper describes how Quanta Cloud Technology (QCT), a long-time Intel® partner, developed the Taiwania 2 and Taiwania 3 supercomputers to meet the research needs of the Taiwan’s academic, industrial, and enterprise users. The Taiwan National Center for High-Performance Computing (NCHC) selected QCT for their expertise in building HPC/AI supercomputers and providing worldwide end-to-end support for solutions from system design, through integration, benchmarking and installation for end users and system integrators to ensure customer success.

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Sponsored by QCT

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