One of the main objectives of the DARPA High Productivity Computing Systems (HPCS) program is to reassess the way we define and measure performance, programmability, portability, robustness and ultimately productivity in the High Performance Computing (HPC) domain. This article describes the Scalable Synthetic Compact Applications (SSCA) benchmark suite, a community product delivered under support of the DARPA HPCS program. The SSCA benchmark suite consists of six benchmarks. The first three SSCA benchmarks are specified and described in this article. The last three are to be developed and will relate to simulation. SSCA #1 Bioinformatics Optimal Pattern Matching stresses integer and character operations (no floating point required) and is compute-limited; SSCA #2 Graph Analysis stresses memory access, uses integer operations, is compute-intensive, and is hard to parallelize on most modern systems; and SSCA #3 Synthetic Aperture Radar Application is computationally taxing, seeks a high rate at which answers are generated, and contains a significant file I/O component. These SSCA benchmarks are envisioned to emerge as complements to current scalable micro-benchmarks and complex real applications to measure high-end productivity and system performance. They are also described in sufficient detail to drive novel HPC programming paradigms, as well as architecture development and testing. The benchmark written and executable specifications are available from www.highproductivity.org.
One of the main objectives of the DARPA High Productivity Computing Systems (HPCS) program1 is to reassess the way we define and measure performance, programmability, portability, robustness and ultimately productivity in the High Performance Computing (HPC) domain. An initiative in this direction is the formulation of the Scalable Synthetic Compact Applications (SSCA)2 benchmark suite. Each SSCA benchmark is composed of multiple related kernels which are chosen to represent workloads within real HPC applications and is used to evaluate and analyze the ease of use of the system, memory access patterns, communication and I/O characteristics. The benchmarks are relatively small to permit productivity testing and programming inreasonable time; and scalable in problem representation and size to allow simulating a run at small scale or executing on a large system at large scale.
Each benchmark written specification presents detailed background and parameters for an untimed data generator and a number of timed application kernels. All of the SSCA benchmarks are intended to be scalable using any of a variety of techniques, a variety of languages, and a variety of machine architectures. Each SSCA includes a number of untimed validation steps to provide checks an implementor can make to gain confidence in the correctness of the implementation.
The SSCA benchmark suite consists of six benchmarks. The first three SSCA benchmarks are specified and described in this article. The last three are to be developed and will relate to simulation.
- Bioinformatics Optimal Pattern Matching: This benchmark focuses on sequence alignment algorithms in computational biology. It stresses integer and character operations, and requires no floating point operations. It is compute-limited, and most of the kernels are embarrassingly parallel. (Section 2)
- Graph Analysis: SSCA #2 is a graph theory benchmark representative of computations in informatics and national security. It is characterized by integer operations, a large memory footprint, and irregular memory access patterns. It is also relatively harder toparallelize compared to the other two SSCAs.
(Section 3) - Synthetic Aperture Radar Application: This benchmark is characteristic of the computations, communication, and taxing data I/O requirements that are found in many types of sensor processing chains. SSCA #3’s principal performance goal is throughput, or in other words, the rate at which answers are generated. The benchmark stresses large block data transfers and memory accesses, and small I/O.
(Section 4)
