SCHEDULE: NOV 15-20, 2015

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Memory Access Patterns: the Missing Piece of the Multi-GPU Puzzle

SESSION: GPU Memory Management

EVENT TYPE: Papers

EVENT TAG(S): Accelerators

TIME: 2:30PM - 3:00PM

SESSION CHAIR(S): Ana Lucia Varbanescu

AUTHOR(S):Tal Ben-Nun, Ely Levy, Amnon Barak, Eri Rubin

ROOM:19AB

ABSTRACT:

With the increased popularity of multi-GPU nodes in modern HPC clusters, it is imperative to develop matching programming paradigms for their efficient utilization.
In order to take advantage of the local GPUs and the low-latency high-throughput
interconnects that link them, programmers need to meticulously adapt parallel applications with respect to load balancing, boundary conditions and device synchronization.
This paper presents MAPS-Multi, an automatic multi-GPU partitioning framework
that distributes the workload based on the underlying memory access patterns. The framework consists of host- and device-level APIs that allow programs to efficiently run on a variety of GPU and multi-GPU architectures.
The framework implements several layers of code optimization, device abstraction, and automatic inference of inter-GPU memory exchanges.
The paper demonstrates that the performance of MAPS-Multi achieves near-linear scaling
on fundamental computational operations, as well as real-world applications in deep learning
and multivariate analysis.

Chair/Author Details:

Ana Lucia Varbanescu (Chair) - University of Amsterdam|

Tal Ben-Nun - Hebrew University of Jerusalem

Ely Levy - Hebrew University of Jerusalem

Amnon Barak - Hebrew University of Jerusalem

Eri Rubin - Hebrew University of Jerusalem

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Paper provided by the ACM Digital Library

Paper also available from IEEE Computer Society