Open, cross-architecture acceleration of data analytics with SYCL and RISC-V

The past few years have witnessed the growth in popularity of two standards for accelerating AI and analytics. On the hardware front, the advent of RISC-V, an open instruction set architecture, has ushered in a new era in standards-based design and customization of microprocessors. On the programming front, SYCL has emerged as a cross-vendor, cross-architecture, data parallel programming model for all types of accelerators. In this work, we take the first steps towards bringing these two standards together to enable a new line of work on fully-open, vendor-neutral, cross-architecture-accelerated database engines by developing SYCLDB--a SYCL-based library of key relational operations that works together with the oneAPI Construction Kit (OCK) to target multi-vendor CPU and accelerator backends. Using SYCLDB, we perform a comparative evaluation with micro and macrobenchmarks to show that SYCLDB can (i) exploit vectorization provided by RVV accelerators, (ii) provide performance on-par with CUDA counterparts on NVIDIA GPUs, and (iii) exploit multithreading in x64 and RISC-V CPUs, all the while using a single code base.