Journal
50TH ANNUAL IEEE/ACM INTERNATIONAL SYMPOSIUM ON MICROARCHITECTURE (MICRO)
Volume -, Issue -, Pages 600-611Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3123939.3123976
Keywords
GPGPU; SIMD; Data Dependency; Thread Block Scheduling; Dataflow
Funding
- NSF [CCF-1423108, CCF-1513201]
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GPUs lack fundamental support for data-dependent parallelism and synchronization. While CUDA Dynamic Parallelism signals progress in this direction, many limitations and challenges still remain. This paper introducesWireframe, a hardware-software solution that enables generalized support for data-dependent parallelism and synchronization. Wireframe enables applications to naturally express execution dependencies across different thread blocks through a dependency graph abstraction at run-time, which is sent to the GPU hardware at kernel launch. At run-time, the hardware enforces the dependencies specified in the dependency graph through a dependencyaware thread block scheduler. Overall, Wireframe is able to improve total execution time up to 65.20% with an average of 45.07%.
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