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Warp-Aware Trace Scheduling for GPUs
| Citations: | 1 - 0 self |
BibTeX
@MISC{Jablin_warp-awaretrace,
author = {James A. Jablin and Thomas B. Jablin and Onur Mutlu and Maurice Herlihy},
title = {Warp-Aware Trace Scheduling for GPUs},
year = {}
}
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Abstract
GPU performance depends not only on thread/warp level parallelism (TLP) but also on instruction-level parallelism (ILP). It is not enough to schedule instructions within ba-sic blocks, it is also necessary to exploit opportunities for ILP optimization beyond branch boundaries. Unfortunately, modern GPUs cannot dynamically carry out such optimiza-tions because they lack hardware branch prediction and can-not speculatively execute instructions beyond a branch. We propose to circumvent these limitations by adapting Trace Scheduling, a technique originally developed for mi-crocode optimization. Trace Scheduling divides code into traces (or paths), and optimizes each trace in a context-independent way. Adapting Trace Scheduling to GPU code requires revisiting and revising each step of microcode Trace Scheduling to attend to branch and warp behavior, identi-fying instructions on the critical path, avoiding warp diver-gence, and reducing divergence time. Here, we propose “Warp-Aware Trace Scheduling ” for GPUs. As evaluated on the Rodinia Benchmark Suite using dynamic profiling, our fully-automatic optimization achieves a geometric mean speedup of 1.10 × on a real system by increasing instructions executed per cycle (IPC) by a har-monic mean of 1.12 × and reducing instruction serialization and total instructions executed.
Keyphrases
warp-aware trace scheduling trace scheduling critical path instruction-level parallelism modern gpus cannot hardware branch prediction ilp optimization thread warp level parallelism identi-fying instruction gpu performance rodinia benchmark suite gpu code ba-sic block fully-automatic optimization instruction serialization branch boundary context-independent way real system geometric mean speedup microcode trace scheduling warp diver-gence total instruction divergence time mi-crocode optimization execute instruction dynamic profiling har-monic mean
