On the Safe Deployment of Matrix Multiplication in Massively Parallel Safety-Related Systems
Javier Fernández; Jon Perez-Cerrolaza; Irune Agirre; Alejandro J. Calderon; Jaume Abella; Francisco J. Cazorla
Deep learning technology has enabled the development of increasingly complex safety-related autonomous systems using high-performance computers, such as graphics processing units (GPUs), which provide the required high computing performance for the execution of parallel computing algorithms, such as matrix&ndash:matrix multiplications (a central computing element of deep learning software libraries). However, the safety certification of parallel computing software algorithms and GPU-based safety-related systems is a challenge to be addressed. For example, achieving the required fault-tolerance and diagnostic coverage for random hardware errors. This paper contributes with a safe matrix&ndash:matrix multiplication software implementation for GPUs with random hardware error-detection capabilities (permanent, transient) that can be used with different architectural patterns for fault-tolerance, and which serves as a foundation for the implementation of safe deep learning libraries for GPUs. The proposed contribution is complementary and can be combined with other techniques, such as algorithm-based fault tolerance. In particular, (i) we provide the high-performance matrix multiplication CUTLASS library with a catalog of diagnostic mechanisms to detect random hardware errors down to the arithmetic operation level: and (ii) we measure the performance impact incurred by the adoption of these mechanisms and their achievable diagnostic coverage with a set of representative matrix dimensions. To that end, we implement these algebraic operations, targeting CUDA cores with single instructions and multiple-thread math instructions in an NVIDIA Xavier NX GPU.
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