Modern GPGPU supports executing multiple tasks with different run time characteristics and resource utilization. Having an efficient execution and resource management policy has been shown to be a critical performance factor when handling the concurrent execution of tasks with different run time behavior. Previous policies either assign equal resources to disparate tasks or allocate resources based on static or standalone behavior profiling. Treating tasks equally cannot efficiently utilize the system resources, while the standalone profiling ignores the correlated impact when running tasks concurrently and could hint incorrect task behavior. This paper addresses the above drawbacks and proposes a heterogeneity aware Selective Bypassing and Mapping (SBM) to manage both computing and cache resources for multiple tasks in a fine-grain manner. The light-weight run time profiling of SBM properly characterizes the disparate behavior of the concurrently executed multiple tasks, and selectively applies suited cache management and workgroup mapping policies to each task. When compared with the previous coarse-grained policies, SBM can achieve an average of 138% and up to 895% performance enhancement. When compared with the state-of-art fine-grained policy, SBM can achieve an average of 58% and up to 378% performance enhancement.

现代GPGPU支持执行具有不同运行时特性和资源利用率的多个任务。在处理具有不同运行时行为的任务的并发执行时，具有有效的执行和资源管理策略已被证明是关键的性能因素。以前的策略要么分配相等的资源来分散任务，要么基于静态或独立的行为分析来分配资源。平等对待任务无法有效利用系统资源，而独立配置文件忽略了同时运行任务时的相关影响，并可能暗示错误的任务行为。本文解决了上述缺点，并提出了一种异构感知选择性旁路和映射（SBM），以精细方式管理多个任务的计算和缓存资源。SBM的轻量级运行时性能分析正确地表征了同时执行的多个任务的不同行为，并选择性地将适合的缓存管理和工作组映射策略应用于每个任务。与以前的粗粒度策略相比，SBM可以平均实现138％的性能提升和高达895％的性能提升。与最新的细粒度策略相比，SBM可以平均提高58％的性能，最高可提高378％的性能。