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SCHEDULE: NOV 16-21, 2014
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Scalable Kernel Fusion for Memory-Bound GPU Applications
SESSION: Accelerators
EVENT TYPE: Papers
TIME: 2:00PM - 2:30PM
SESSION CHAIR: Mitsuhisa Sato
AUTHOR(S):Mohamed Wahib, Naoya Maruyama
ROOM:388-89-90
ABSTRACT:
GPU implementations of HPC applications relying on finite difference methods can include tens of kernels that are memory-bound. Kernel fusion can improve the performance by reducing data traffic to off-chip memory; kernels that share data arrays are fused to larger kernels where on-chip cache is used to hold the data reused by instructions originating from different kernels. The main challenges are: a) Searching for the optimal kernel fusions while constrained by data dependences and kernels' precedences and, b) Effectively applying kernel fusion to achieve speedup. This paper introduces a problem definition and a scalable method for searching the space of possible kernel fusions to identify optimal kernel fusions for large problem sizes. The paper also introduces a codeless performance upper-bound projection to achieve effective fusions. Results show how using the proposed kernel fusion method improved the performance of two real-world applications containing tens of kernels by 1.35x and 1.2x.
Chair/Author Details:
Mitsuhisa Sato (Chair) - University of Tsukuba
Mohamed Wahib - RIKEN
Naoya Maruyama - RIKEN
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