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SNIA Developer Conference September 15-17, 2025 | Santa Clara, CA

David Emberson

Senior Distinguished Technologis

HPE

David Emberson is Senior Distinguished Technologist for HPC System Architecture, where he is working on future memory system designs for HPE Cray systems. He began his career at MIT's Digital Systems Laboratory, where he built one of the first portable computers in 1975. He has held positions at Prime Computer, Megatest, Ametek Computer Research, and Sun Microsystems. At Sun, Mr. Emberson was a member of the SPARC architecture committee, managed the SparcStation 10 and SparcStation 20 programs, and was Senior Director at SunLabs. His consulting clients have included the Hypertransport Consortium, AMD, Intel, Atheros, PathScale, Qlogic and numerous startup companies. At HPE he was Technical Director of HPE's PathForward program for the Department of Energy's Exascale Computing Program. His current research is in memory system design for HPC systems. He serves on the JEDEC J42.2 (HBM) committee and is a Senior Member of IEEE. Mr. Emberson has a B.S. in Electrical Engineering from MIT. He holds nineteen patents.

Global Distributed Client-side Caching for HPC/AI Storage Systems

Submitted by Anonymous (not verified) on

HPC and AI workloads require processing massive datasets and executing complex computations at exascale speeds to deliver time-critical insights. In distributed environments where storage systems coordinate and share results, communication overhead can become a critical bottleneck. This challenge underscores the need for storage solutions that deliver scalable, parallel access with microsecond latencies from compute clusters. Caching can help reduce communication costs when implemented on either servers or clients.

Fabric Attached Memory – Hardware and Software Architecture

Submitted by Anonymous (not verified) on

HPC architectures increasingly handle workloads where the working data set cannot be easily partitioned or is too large to fit into node local memory. We have defined a system architecture and a software stack to enable large data sets to be held in fabric-attached memory (FAM) that is accessible to all compute nodes across a Slingshot-connected HPC cluster, thus providing a new approach to handling large data sets.

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