Classification of OpenCL Kernels for accelerating Java Multi-agent Simulation

Abstract

Java-based multi-agent simulation (MAS) can be offloaded to graphical processing units (GPU) and other OpenCL accelerators to achieve many hundredfold speedups. However, the performance gain from the accelerated code depends strongly on whether the computation (kernels) have been scheduled to the appropriate devices. Thus, accelerating Java MAS may not lead to a sustainable speedup. This paper proposes a method for a kernel classifier to specify suitable devices to execute OpenCL kernels. The classifier can identify suitable OpenCL devices for kernels based on the static and dynamic characteristics of the code of the kernels. Kernels are grouped by their suitability for particular devices using the multiclass support virtual machine technique. After that, kernels are scheduled to an appropriate task queue. Kernel scheduling based on the proposed technique is compared against the firstcome-first-serve (FCFS) technique and against oracle scheduling when handling eight kernels. Our results show that, using the proposed method, all kernels finished execution 45 percent sooner than using the FCFS technique. However, the overall execution time was 22.5 percent longer than with oracle scheduling. Our results seem to confirm that kernel classification techniques might contribute towards sustainable high performance in accelerated Java-based MAS models.