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Interview: Mark Papermaster, CTO and EVP, Technology and Engineering, AMD

In this interview, Mark Papermaster, CTO and EVP, Technology and Engineering from AMD describes the company’s presence in the HPC space along with new trends in the industry. At a higher level, Mark also offers his views of the semiconductor industry in general as well as areas of innovation that AMD plans to cultivate. The discussion then turns to the exascale era of computing.

From Forty Days to Sixty-five Minutes without Blowing Your Budget Thanks to Gigaio Fabrex

In this sponsored post, Alan Benjamin, President and CEO of GigaIO, discusses how the ability to attach a group of resources to one server, run the job(s), and reallocate the same resources to other servers is the obvious solution to a growing problem: the incredible rate of change of AI and HPC applications is accelerating, triggering the need for ever faster GPUs and FPGAs to take advantage of the new software updates and new applications being developed.

Fast Track your AI Workflows

In this special guest feature, our friends over at Inspur write that for new workloads that are highly compute intensive, accelerators are often required. Accelerators can speed up the computation and allow for AI and ML algorithms to be used in real time. Inspur is a leading supplier of solutions for HPC and AI/ML workloads.

2nd Generation Intel® Xeon® Scalable Processors Demonstrate Amazing HPC Performance

In this guest article, our friends at Intel discuss how benchmarks show key workloads average 31% better on Intel Xeon Platinum 9282 than AMD EYPC “Rome” 7742. Intel analysis provides strong evidence that the 2nd Generation Intel Xeon Scalable Processor (Cascade Lake “CLX”) architecture provides dramatic performance for real-world workloads. An impressive array of benchmarks shows 2S systems built with Intel’s 56 core processors (Intel Xeon Platinum 9282 processor) solidly ahead of systems built with AMD’s 64 core processors (AMD EYPC 7742).

Optimizing in a Heterogeneous World is (Algorithms x Devices)

In this guest article, our friends at Intel discuss how CPUs prove better for some important Deep Learning. Here’s why, and keep your GPUs handy! Heterogeneous computing ushers in a world where we must consider permutations of algorithms and devices to find the best platform solution. No single device will win all the time, so we need to constantly assess our choices and assumptions.