high performance computing on graphics processing units: hgpu.org

To increase performance and efficiency, systems use FPGAs as reconfigurable accelerators. A key challenge in designing these systems is partitioning computation between processors and an FPGA. An appropriate division of labor may be difficult to predict in advance and require experiments and measurements. When an investigation requires rewriting part of the system in a new language or with a new programming model, its high cost can retard the study of different configurations. A single-language system with an appropriate programming model and compiler that targets both platforms simplifies this exploration to a simple recompile with new compiler directives. This work introduces StreamBlocks, an open-source compiler and runtime that uses the CAL dataflow programming language to partition computations across heterogeneous (CPU/accelerator) platforms. Because of the dataflow model’s semantics and the CAL language, StreamBlocks can exploit both thread parallelism in multi-core CPUs and the inherent parallelism of FPGAs. StreamBlocks supports exploring the design space with a profile-guided tool that helps identify the best hardware-software partitions.