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Solving Heterogeneous Programming Challenges with SYCL

December 8, 2021

In the first of a series of guest posts on heterogenous computing, James Reinders, who returned to Intel last year after a short “retirement,” considers how SYCL will contribute to a heterogeneous future for C++. Reinders digs into SYCL from multiple angles... Read more…

15 Slides on Programming Aurora and Exascale Systems

May 7, 2020

Sometime in 2021, Aurora, the first planned U.S. exascale system, is scheduled to be fired up at Argonne National Laboratory. Cray (now HPE) and Intel are the k Read more…

DARPA Looks to Propel Parallelism

September 4, 2019

As Moore’s law runs out of steam, new programming approaches are being pursued with the goal of greater hardware performance with less coding. The Defense Advanced Projects Research Agency is launching a new programming effort aimed at leveraging the benefits of massive distributed parallelism with less sweat. Read more…

An Overview of ‘OpenACC for Programmers’ from the Book’s Editors

June 20, 2018

In an era of multicore processors coupled with manycore accelerators in all kinds of devices from smartphones all the way to supercomputers, it is important to Read more…

GTC18 Research Highlight: Programming a Hybrid CPU-GPU Cluster Using Unicorn

March 27, 2018

Unicorn is a parallel programming framework that provides a simple way to program multi-node clusters with CPUs and GPUs, and potentially other compute devices. Read more…

Optimizing Codes for Heterogeneous HPC Clusters Using OpenACC

July 3, 2017

Looking at the Top500 and Green500 ranks, one clearly realizes that most HPC systems are heterogeneous architecture using COTS (Commercial Off-The-Shelf) hardware, combining traditional multi-core CPUs with massively parallel accelerators, such as GPUs and MICs. With processor frequencies now hitting a solid wall, the only truly open avenue for riding today the Moore’s law is increasing hardware parallelism in several different ways: more computing nodes, more processors in each node, more cores within each processor, and longer vector instructions in each core. Read more…

LOLCODE: I Can Has Supercomputer?

April 5, 2017

What programming model refers to threads as friends and uses types like NUMBR (integer), NUMBAR (floating point), YARN (string), and TROOF (Boolean)? That would Read more…

MIT’s Multicore Swarm Architecture Advances Ordered Parallelism

July 21, 2016

A relatively new architecture explicitly designed for parallelism – Swarm – based on work at MIT has shown promise for substantially speeding up classes of Read more…

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QCT QoolRack: An Optimized Liquid Cooling Solution for HPC/AI Workloads

Data center infrastructure running AI and HPC workloads requires powerful microprocessor chips and the use of CPUs, GPUs, and acceleration chips to carry out compute intensive tasks. AI and HPC processing generate excessive heat which results in higher data center power consumption and additional data center costs.

Data centers traditionally use air cooling solutions including heatsinks and fans that may not be able to reduce energy consumption while maintaining infrastructure performance for AI and HPC workloads. Liquid cooled systems will be increasingly replacing air cooled solutions for data centers running HPC and AI workloads to meet heat and performance needs.

QCT worked with Intel to develop the QCT QoolRack, a rack-level direct-to-chip cooling solution which meets data center needs with impressive cooling power savings per rack over air cooled solutions, and reduces data centers’ carbon footprint with QCT QoolRack smart management.

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