Scalable heterogeneous computing systems, which are composed of a mix of compute devices, such as commodity multicore processors, graphics processors, reconfigurable processors, and others, are gaining attention as one approach to continuing performance improvement while managing the new challenge of energy efficiency. As these systems become more common, it is important to be able to compare and contrast architectural designs and programming systems in a fair and open forum. To this end, we have designed the Scalable HeterOgeneous Computing benchmark suite (SHOC). SHOC's initial focus is on systems containing graphics processing units (GPUs) and multi-core processors, and on the new OpenCL programming standard. SHOC is a spectrum of programs that test the performance and stability of these scalable heterogeneous computing systems. At the lowest level, SHOC uses microbenchmarks to assess architectural features of the system. At higher levels, SHOC uses application kernels to determine system-wide performance including many system features such as intranode and internode communication among devices. SHOC includes benchmark implementations in both OpenCL and CUDA in order to provide a comparison of these programming models.
Developers of application codes for massively parallel computer systems face daunting performance tuning and optimization problems that must be solved if massively parallel systems are to fulfill their promise. Recording and analyzing the dynamics of application program, system software, and hardware interac.tions is the key to understanding and the prerequisite to performance tuning, but this instrumentation and analysis must not unduly perturb program execution. Pablo is a performance analysis environment designed t o provide unobtrusive performance data capture, analysis, and presentation across a wide variety of scalable parallel systems. Current efforts include dynamic statistical clustering t o reduce the volume of data that must be captured and complete performance data immersion via head-mounted displays.
As supercomputer performance approached and then surpassed the petaflop level, I/O performance has become a major performance bottleneck for many scientific applications. Several tools exist to collect I/O traces to assist in the analysis of I/O performance problems. However, these tools either produce extremely large trace files that complicate performance analysis, or sacrifice accuracy to collect high-level statistical information. We propose a multi-level trace generator tool, ScalaIOTrace, that collects traces at several levels in the HPC I/O stack. ScalaIOTrace features aggressive trace compression that generates trace files of near constant size for regular I/O patterns and orders of magnitudes smaller for less regular ones. This enables the collection of I/O and communication traces of applications running on thousands of processors.Our contributions also include automated trace analysis to collect selected statistical information of I/O calls by parsing the compressed trace on-the-fly and time-accurate replay of communication events with MPI-IO calls. We evaluated our approach with
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