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High-Performance GPU-to-CPU Transpilation and Optimization via High-Level Parallel Constructs.

, , , , , and . PPoPP, page 119-134. ACM, (2023)

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Tapir: Embedding Recursive Fork-join Parallelism into LLVM's Intermediate Representation., , and . ACM Trans. Parallel Comput., 6 (4): 19:1-19:33 (2019)AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement Learning., , , , , , and . CoRR, (2019)Understanding Automatic Differentiation Pitfalls., , , , , and . CoRR, (2023)Tapir: Embedding Fork-Join Parallelism into LLVM's Intermediate Representation., , and . PPoPP, page 249-265. ACM, (2017)High-Performance GPU-to-CPU Transpilation and Optimization via High-Level Parallel Constructs., , , , , and . CoRR, (2022)The Quantum Tortoise and the Classical Hare: A simple framework for understanding which problems quantum computing will accelerate (and which it will not)., , and . CoRR, (2023)Retargeting and Respecializing GPU Workloads for Performance Portability., , , , and . CGO, page 119-132. IEEE, (2024)Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients., and . NeurIPS, (2020)High-Performance GPU-to-CPU Transpilation and Optimization via High-Level Parallel Constructs., , , , , and . PPoPP, page 119-134. ACM, (2023)Scalable Automatic Differentiation of Multiple Parallel Paradigms through Compiler Augmentation., , , , , , , and . SC, page 60:1-60:18. IEEE, (2022)