Valar: A Benchmark Suite to Study the Dynamic Behavior of Heterogeneous Systems

Perhaad Mistry, Yash Ukidave, Dana Schaa, David Kaeli
Northeastern University Boston, MA
6th Workshop on General Purpose Processor Using Graphics Processing Units (GPGPU-6), 2013


   title={Valar: a benchmark suite to study the dynamic behavior of heterogeneous systems},

   author={Mistry, Perhaad and Ukidave, Yash and Schaa, Dana and Kaeli, David},

   booktitle={Proceedings of the 6th Workshop on General Purpose Processor Using Graphics Processing Units},





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Heterogeneous systems have grown in popularity within the commercial platform and application developer communities. We have seen a growing number of systems incorporating CPUs, Graphics Processors (GPUs) and Accelerated Processing Units (APUs combine a CPU and GPU on the same chip). These emerging class of platforms are now being targeted to accelerate applications where the host processor (typically a CPU) and compute device (typically a GPU) co-operate on a computation. In this scenario, the performance of the application is not only dependent on the processing power of the respective heterogeneous processors, but also on the efficient interaction and communication between them. To help architects and application developers to quantify many of the key aspects of heterogeneous execution, this paper presents a new set of benchmarks called the Valar. The Valar benchmarks are applications specifically chosen to study the dynamic behavior of OpenCL applications that will benefit from host-device interaction. We describe the general characteristics of our benchmarks, focusing on specific characteristics that can help characterize heterogeneous applications. For the purposes of this paper we focus on OpenCL as our programming environment, though we envision versions of Valar in additional heterogeneous programming languages. We profile the Valar benchmarks based on their mapping and execution on different heterogeneous systems. Our evaluation examines optimizations for host-device communication and the effects of closely-coupled execution of the benchmarks on the multiple OpenCL devices present in heterogeneous systems.
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