Lowering IrGL to CUDA
Sreepathi Pai, Keshav Pingali
TL;DR
This paper introduces IrGL, an intermediate representation designed for irregular GPU programs, detailing its constructs, usage examples, and compilation process to CUDA via the Galois GPU compiler.
Contribution
It presents IrGL as a new explicit parallel IR for irregular programs and explains its compilation to CUDA, enhancing GPU programming flexibility.
Findings
IrGL effectively captures irregular parallelism.
The Galois GPU compiler successfully translates IrGL to CUDA.
IrGL simplifies programming for irregular GPU workloads.
Abstract
The IrGL intermediate representation is an explicitly parallel representation for irregular programs that targets GPUs. In this report, we describe IrGL constructs, examples of their use and how IrGL is compiled to CUDA by the Galois GPU compiler.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsParallel Computing and Optimization Techniques · Distributed and Parallel Computing Systems · Advanced Data Storage Technologies