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Deep Learning GPU Cluster

In this whitepaper, our friends over at Lambda walk you through the Lambda Echelon multi-node cluster reference design: a node design, a rack design, and an entire cluster level architecture. This document is for technical decision-makers and engineers. You’ll learn about the Echelon’s compute, storage, networking,  power distribution, and thermal design. This is not a cluster administration handbook, this is a high level technical overview of one possible system architecture.

Choosing the Best Data Flow Design for GPU Accelerated Applications

In this sponsored article from our friends over at Supermicro, we discusses how deciding on the correct type of GPU accelerated computation hardware depends on many factors. One particularly important aspect is the data flow patterns across the PCIe bus and between GPUs and Intel® Xeon® Scalable processors. These factors, along with some application insights are explored below.

Practical Hardware Design Strategies for Modern HPC Workloads – Part 3

This special research report sponsored by Tyan discusses practical hardware design strategies for modern HPC workloads. As hardware continued to develop, technologies like multi-core, GPU, NVMe, and others have allowed new application areas to become possible. These application areas include accelerator assisted HPC, GPU based Deep learning, and Big Data Analytics systems. Unfortunately, implementing a general purpose balanced system solution is not possible for these applications. To achieve the best price-to-performance in each of these application verticals, attention to hardware features and design is most important.

Practical Hardware Design Strategies for Modern HPC Workloads – Part 2

This special research report sponsored by Tyan discusses practical hardware design strategies for modern HPC workloads. As hardware continued to develop, technologies like multi-core, GPU, NVMe, and others have allowed new application areas to become possible. These application areas include accelerator assisted HPC, GPU based Deep learning, and Big Data Analytics systems. Unfortunately, implementing a general purpose balanced system solution is not possible for these applications. To achieve the best price-to-performance in each of these application verticals, attention to hardware features and design is most important.

Practical Hardware Design Strategies for Modern HPC Workloads

This special research report sponsored by Tyan discusses practical hardware design strategies for modern HPC workloads. As hardware continued to develop, technologies like multi-core, GPU, NVMe, and others have allowed new application areas to become possible. These application areas include accelerator assisted HPC, GPU based Deep learning, and Big Data Analytics systems. Unfortunately, implementing a general purpose balanced system solution is not possible for these applications. To achieve the best price-to-performance in each of these application verticals, attention to hardware features and design is most important.

Practical Hardware Design Strategies for Modern HPC Workloads

Many new technologies used in High Performance Computing (HPC) have allowed new application areas to  become possible. Advances like multi-core, GPU, NVMe, and others have created application verticals that  include accelerator assisted HPC, GPU based Deep Learning, Fast storage and parallel file systems, and Big  Data Analytics systems. In this special insideHPC technology guide sponsored by our friends over at Tyan, we look at practical hardware design strategies for modern HPC workloads.

Call for Papers: EuroPar 2017 in Santiago de Compostela

The Euro-Par 2017 conference has issued its Call for Papers. The conference takes place Aug. 28 – Sept. 1, 2017 in Santiago de Compostela, Spain. Euro-Par is the prime European conference covering all aspects of parallel and distributed processing, ranging from theory to practice, from small to the largest parallel and distributed systems and infrastructures, from […]