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ParDiff: Efficiently Parallelizing Reverse-Mode Automatic Differentiation with Direct Indexing
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DOI：10.1145/3774934.3786418.png)
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Abstract
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Automatic Differentiation (AD) is a technique that computes the derivatives of numerical programs by systematically applying the chain rule, playing a critical role in domains such as machine learning, simulation, and control systems. However, parallelizing differentiated programs remains a significant challenge due to the conflict between tapes (a data structure for intermediate variable storage) and summations: the differentiation process inherently introduces inter-thread summation patterns, which require prohibitively expensive atomic operations; and traditional tape designs tightly couple data retrieval with the program's control flow, preventing code restructuring needed to eliminate these costly dependencies. To address these challenges, we present ParDiff, a novel AD system with a direct-indexed tape design, which enables summation-aware loop transformations and various parallel schemes for differentiated programs. This results in a higher degree of parallelization, less synchronization, and reduced inter-thread data movement. We conduct comprehensive experiments on both multi-core CPUs and GPUs. Results show that ParDiff delivers up to 483.21x (geometric mean: 30.88x) speedup over the state-of-the-art fully-AD system, Enzyme. It also achieves a speedup of 2.05x and 2.06x over PyTorch on CPU and GPU, respectively. The source code is publicly available at https://github.com/roastduck/FreeTensor.
Keywords:
AD
Tape
Parallel Computing
Direct Indexing
Journal
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Cited Papers
Adjoint computations by algorithmic differentiation of a parallel solver for time-dependent PDEs
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