November 15, 2023
Software implementation in high-performance computing is getting more fragmented as organizations opt for tools in their walled garden environments. Howeve Read more…
March 10, 2022
In this regular feature, HPCwire highlights newly published research in the high-performance computing community and related domains. From parallel programmin Read more…
March 8, 2022
AMD/Xilinx has released an improved version of its VCK5000 AI inferencing card along with a series of competitive benchmarks aimed directly at Nvidia’s GPU line. AMD says the new VCK5000 has 3x better performance than earlier versions and delivers 2x TCO over Nvidia T4. AMD also showed favorable benchmarks against several Nvidia GPUs, claiming its VCK5000 achieved... Read more…
July 1, 2021
AMD’s planned acquisition of FPGA maker Xilinx is now in the hands of Chinese regulators after needed antitrust approvals for the $35 billion deal were receiv Read more…
July 31, 2020
A machine programming framework for heterogeneous computing championed by Intel Corp. and university partners is built around an automated engine that analyzes Read more…
November 27, 2019
In this bimonthly feature, HPCwire highlights newly published research in the high-performance computing community and related domains. From parallel programming to exascale to quantum computing, the details are here. Read more…
September 12, 2019
In this bimonthly feature, HPCwire highlights newly published research in the high-performance computing community and related domains. From parallel programm Read more…
October 17, 2018
Three Chinese infrastructure vendors are embracing FPGA technology as a way of accelerating datacenter workloads. FPGA specialist Xilinx Inc. announced during a developer forum in Beijing this week that Alibaba Cloud, Huawei and server vendor Inspur are rolling out datacenter platforms based on the chip maker’s FPGA-as-a-service model. Among the datacenter workloads being targeted is AI inference, the partners said Tuesday. Read more…
Making the Most of Today’s Cloud-First Approach to Running HPC and AI Workloads With Penguin Scyld Cloud Central™
Bursting to cloud has long been used to complement on-premises HPC capacity to meet variable compute demands. But in today’s age of cloud, many workloads start on the cloud with little IT or corporate oversight. What is needed is a way to operationalize the use of these cloud resources so that users get the compute power they need when they need it, but with constraints that take costs and the efficient use of existing compute power into account. Download this special report to learn more about this topic.
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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