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GROPHECY: GPU Performance Projection from CPU Code Skeletons
SESSION: GPU Optimizations
EVENT TYPE: Paper
TIME: 11:30AM - 12:00PM
AUTHOR(S):Jiayuan Meng, Vitali Morozov, Kalyan Kumaran, Venkatram Vishwanath, Thomas Uram
ROOM:TCC 303
ABSTRACT: We propose a GPU projection framework that can estimate the
performance benefit of GPU acceleration
without actual GPU programming or hardware.
Users only need to skeletonize pieces of CPU code that are targets
for GPU acceleration. The framework will then transform
the code skeleton in various ways to mimic tuned GPU code
with characteristics resembling real implementations. The synthesized
characteristics are used by an existing analytical model
to project GPU performance. The cost and benefit of GPU development
can then be estimated according to the transformed
code skeleton that yields the best projected performance. With
this framework, users can leap towards GPU acceleration only
when the cost-benefit makes sense. We validate the framework
using micro-benchmarks as well as data-parallel codes in legacy
high performance computing applications. The measured performance
of manually tuned codes deviates from the projected
performance by 17% in geometric mean.
Chair/Author Details:
Jiayuan Meng - Argonne National Laboratory
Vitali Morozov - Argonne National Laboratory
Kalyan Kumaran - Argonne National Laboratory
Venkatram Vishwanath - Argonne National Laboratory