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Computing, Networking, Storage and Analysis
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SCHEDULE: NOV 15-20, 2015

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ELF: Maximizing Memory-Level Parallelism for GPUs with Coordinated Warp and Fetch Scheduling

SESSION: GPU Memory Management


EVENT TAG(S): Accelerators

TIME: 2:00PM - 2:30PM

SESSION CHAIR(S): Ana Lucia Varbanescu

AUTHOR(S):Jason Jong Kyu Park, Yongjun Park, Scott Mahlke



Graphics processing units (GPUs) are increasingly utilized
as throughput engines in the modern computer systems.
GPUs rely on fast context switching between thousands
of threads to hide long latency operations, however, they still
stall due to the memory operations. To minimize the stalls,
memory operations should be overlapped with other operations
as much as possible to maximize memory-level parallelism (MLP).
In this paper, we propose Earliest Load First (ELF) warp scheduling,
which maximizes the MLP by giving higher priority to the warps
that have the fewest instructions to the next memory load.
ELF utilizes the same warp priority for the fetch scheduling
so that both are coordinated. We also show that ELF reveals
its full benefits when there are fewer memory conflicts and fetch stalls.
Evaluations show that ELF can improve the performance by 4.1%
and achieve total improvement of 11.9% when used with other
techniques over commonly-used greedy-then-oldest scheduling.

Chair/Author Details:

Ana Lucia Varbanescu (Chair) - University of Amsterdam|

Jason Jong Kyu Park - University of Michigan

Yongjun Park - Hongik University

Scott Mahlke - University of Michigan

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Paper provided by the ACM Digital Library

Paper also available from IEEE Computer Society