With the rapid advancement of artificial intelligence and high-performance computing,heterogeneous computing platforms have evolved to encompass increasingly diverse architectures.While SYCL,an open standard for heter...With the rapid advancement of artificial intelligence and high-performance computing,heterogeneous computing platforms have evolved to encompass increasingly diverse architectures.While SYCL,an open standard for heterogeneous programming,has gained widespread adoption,its mainstream implementations(such as DPC++and AdaptiveCpp)primarily target SIMT-architecture devices like GPUs,presenting substantial challenges when adapting to specialized accelerators such as the Cambricon MLU,which employs a fundamentally different SIMD execution model.This cross-programming-model extension encounters two critical challenges:(1)bridging the programming abstraction gap between SIMT’s thread-level parallelism and SIMD’s data-level parallelism;and(2)harmonizing SYCL’s unified memory model with device-specific memory architectures.This paper proposes a novel cross-programming-model SYCL extension methodology to achieve full SYCL support for SIMD architectures,demonstrated through a comprehensive implementation for the Cambricon MLU platform.Our approach introduces MLU-specific vector programming interfaces while maintaining compatibility with the SYCL standard,enabling seamless integration of SIMD-based accelerators into the SYCL ecosystem.To validate our methodology,we integrated the extended SYCL-MLU implementation into PaddlePaddle’s CINN compiler,achieving a geometric mean performance improvement of 9.14%across representative neural networks,including ResNet,YOLOv3,and BERT.This research significantly broadens the application scope of SYCL in heterogeneous programming and provides a systematic methodology for extending SYCL to other SIMD-based hardware platforms.展开更多
基金supported by the Beijing Science and Technology Planning Project(Grant No.Z231100010323007).
文摘With the rapid advancement of artificial intelligence and high-performance computing,heterogeneous computing platforms have evolved to encompass increasingly diverse architectures.While SYCL,an open standard for heterogeneous programming,has gained widespread adoption,its mainstream implementations(such as DPC++and AdaptiveCpp)primarily target SIMT-architecture devices like GPUs,presenting substantial challenges when adapting to specialized accelerators such as the Cambricon MLU,which employs a fundamentally different SIMD execution model.This cross-programming-model extension encounters two critical challenges:(1)bridging the programming abstraction gap between SIMT’s thread-level parallelism and SIMD’s data-level parallelism;and(2)harmonizing SYCL’s unified memory model with device-specific memory architectures.This paper proposes a novel cross-programming-model SYCL extension methodology to achieve full SYCL support for SIMD architectures,demonstrated through a comprehensive implementation for the Cambricon MLU platform.Our approach introduces MLU-specific vector programming interfaces while maintaining compatibility with the SYCL standard,enabling seamless integration of SIMD-based accelerators into the SYCL ecosystem.To validate our methodology,we integrated the extended SYCL-MLU implementation into PaddlePaddle’s CINN compiler,achieving a geometric mean performance improvement of 9.14%across representative neural networks,including ResNet,YOLOv3,and BERT.This research significantly broadens the application scope of SYCL in heterogeneous programming and provides a systematic methodology for extending SYCL to other SIMD-based hardware platforms.
