Fine-grain task aggregation and coordination on GPUs

Abstract

In general-purpose graphics processing unit (GPGPU) computing, data is processed by concurrent threads execut-ing the same function. This model, dubbed single-instruction/multiple-thread (SIMT), requires programmers to coordinate the synchronous execution of similar opera-tions across thousands of data elements. To alleviate this programmer burden, Gaster and Howes outlined the chan-nel abstraction, which facilitates dynamically aggregating asynchronously produced fine-grain work into coarser-grain tasks. However, no practical implementation has been proposed To this end, we propose and evaluate the first channel im-plementation. To demonstrate the utility of channels, we present a case study that maps the fine-grain, recursive task spawning in the Cilk programming language to channels by representing it as a flow graph. To support data-parallel recursion in bounded memory, we propose a hardware mechanism that allows wavefronts to yield their execution resources. Through channels and wavefront yield, we im-plement four Cilk benchmarks. We show that Cilk can scale with the GPU architecture, achieving speedups of as much as 4.3x on eight compute units