General-purpose graphics processing units (GPGPUs) have been widely adapted to the industry due to the high parallelism of graphics processing units (GPUs) compared with central processing units (CPUs). Especially, a GPGPU device has been adopted for various scientific workloads which have high parallelism. To handle the ever increasing demand, multiple applications are often run simultaneously in multiple GPGPU devices. However, when multiple applications are running concurrently, the overall performance of GPGPU devices varies significantly due to the different characteristics of GPGPU applications. To improve the efficiency, it is critical to anticipate the performance of applications and find optimal scheduling policy. In this paper, we analyze various types of scientific applications and identify factors that impact the performance during the concurrent execution of the applications in GPGPU devices. Our analysis results show that each application has distinct characteristic. By considering distinct characteristics of applications, a certain combination of applications has better performance compared with the others when executed concurrently in multiple GPGPU devices. Based on the finding of our analysis, we propose a simulator which predicts the performance of GPGPU devices when multiple applications are running concurrently. Our simulator collects performance metrics during the execution of applications and predicts the performance of certain combinations using the performance metrics. The experimental result shows that the best combination of applications can increase the performance by 39.44% and 65.98% compared with the average of combinations and the worst case, respectively when using a single GPGPU device. When utilizing multiple GPGPU devices, our result shows that the performance improve can be 24.78% and 39.32% compared with the average and the worst combinations, respectively.

通用图形处理单元（GPGPU）由于图形处理单元（GPU）与中央处理单元（CPU）的高度并行性而已广泛应用于该行业。特别是，GPGPU设备已用于具有高度并行性的各种科学工作负载。为了满足不断增长的需求，通常会在多个GPGPU设备中同时运行多个应用程序。但是，当多个应用程序同时运行时，由于GPGPU应用程序的不同特性，GPGPU设备的整体性能将有很大差异。为了提高效率，预测应用程序的性能并找到最佳的调度策略至关重要。在本文中，我们分析了各种类型的科学应用程序，并确定了在GPGPU设备中并发执行应用程序期间影响性能的因素。我们的分析结果表明，每个应用程序都具有独特的特性。通过考虑应用程序的独特特性，与多个应用程序同时在多个GPGPU设备中执行时，某些应用程序组合具有更好的性能。基于我们的分析结果，我们提出了一个模拟器，该模拟器可以在多个应用程序同时运行时预测GPGPU设备的性能。我们的模拟器在应用程序执行期间收集性能指标，并使用性能指标预测某些组合的性能。实验结果表明，与单个应用程序的平均组合和最坏情况相比，最佳应用程序组合可以将性能分别提高39.44％和65.98％。当使用多个GPGPU设备时，我们的结果表明，与平均和最差组合相比，性能提高分别为24.78％和39.32％。