Generating Efficient Parallel Code from the RVC-CAL Dataflow Language

Rafique, Omair; Krebs, Florian; Schneider, Klaus

doi:10.1109/dsd.2019.00035

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…In both works, the first step is to classify each actor that composes the program (in other words, it is determined whether an actor is static, cyclo-static, or dynamic); then, only the narrowest Static Dataflow Actors (SDFs) (i.e., those for which execution and scheduling behavior can be computed during the compile-time design stage) are executed on the GPU platform, while the dynamic actors [26] are mapped to the available CPU cores. A similar approach is illustrated in [27], where RVC-CAL dataflow programs are first transformed into a high-level SYCL [28] representation. SYCL is a cross-platform C++ abstraction layer that allows source code for heterogeneous processors to be expressed using the OpenCL API.…”

Section: Related Workmentioning

confidence: 99%

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Dynamic SIMD Parallel Execution on GPU from High-Level Dataflow Synthesis

Bloch

Casale-Brunet

Mattavelli

2022

JLPEA

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Developing and fine-tuning software programs for heterogeneous hardware such as CPU/GPU processing platforms comprise a highly complex endeavor that demands considerable time and effort of software engineers and requires evaluating various fundamental components and features of both the design and of the platform to maximize the overall performance. The dataflow programming approach has proven to be an appropriate methodology for reaching such a difficult and complex goal for the intrinsic portability and the possibility of easily decomposing a network of actors on different processing units of the heterogeneous hardware. Nonetheless, such a design method might not be enough on its own to achieve the desired performance goals, and supporting tools are useful to be able to efficiently explore the design space so as to optimize the desired performance objectives. This article presents a methodology composed of several stages for enhancing the performance of dataflow software developed in RVC-CAL and generating low-level implementations to be executed on GPU/CPU heterogeneous hardware platforms. The stages are composed of a method for the efficient scheduling of parallel CUDA partitions, an optimization of the performance of the data transmission tasks across computing kernels, and the exploitation of dynamic programming for introducing SIMD-capable graphics processing unit systems. The methodology is validated on both the quantitative and qualitative side by means of dataflow software application examples running on platforms according to various different mapping configurations.

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Section: Related Workmentioning

confidence: 99%

“…SYCL is a cross-platform C++ abstraction layer that allows source code for heterogeneous processors to be expressed using the OpenCL API. The approach illustrated in [27] is also based on using the SIMD nature of GPUs to run multiple consecutive executions of the same action in parallel when there are data available for its execution.…”

Section: Related Workmentioning

confidence: 99%