Modern High Performance Computing (HPC) sys-tems are built with innovative system architectures and novel programming models to further push the speed limit of computing. The increased complexity poses challenges for performance portability and performance evaluation. The Standard Perfor-mance Evaluation Corporation (SPEC) has a long history of producing industry-standard benchmarks for modern computer systems. SPEC's newly released SPEChpc 2021 benchmark suites, developed by the High Performance Group, are a bold attempt to provide a fair and objective benchmarking tool designed for state-of-the-art HPC systems. With the support of multiple host and accelerator programming models, the suites are portable across both homogeneous and heterogeneous architectures. Different workloads are developed to fit system sizes ranging from a few compute nodes to a few hundred compute nodes. In this work we present our first experiences in performance benchmarking the new SPEChpc2021 suites and evaluate their portability and basic performance characteristics on various popular and emerging HPC architectures, including x86 CPU, NVIDIA GPU, and AMD GPU. This study provides a first-hand experience of executing the SPEChpc 2021 suites at scale on production HPC systems, discusses real-world use cases, and serves as an initial guideline for using the benchmark suites.

The Open ACC standard has been developed to simplify parallel programming of heterogeneous systems. Based on a set of high-level compiler directives it allows application developers to offload code regions from a host CPU to an accelerator without the need for low-level programming with CUDA or Open CL. Details are implicit in the programming model and managed by Open ACC API-enabled compilers and runtimes. However, it is still possible for the application developer to explicitly specify several performance-related details for the execution. To tune an Open ACC program and efficiently utilize available hardware resources, sophisticated performance analysis tools are required. In this paper we present a framework for detailed analysis of Open ACC applications. We describe new analysis capabilities introduced with an Open ACC tools interface and depict the integration of performance analysis for low-level programming models. As proof of concept we implemented the concept into the measurement infrastructure Score-P and the trace browser Vampir. This provides the program developer with a clearer understanding of the dynamic runtime behavior of the application and for systematic identification of potential bottlenecks.

We present a particle-in-cell simulation of the relativistic Kelvin-Helmholtz Instability (KHI) that for the first time delivers angularly resolved radiation spectra of the particle dynamics during the formation of the KHI. This enables studying the formation of the KHI with unprecedented spatial, angular and spectral resolution. Our results are of great importance for understanding astrophysical jet formation and comparable plasma phenomena by relating the particle motion observed in the KHI to its radiation signature.

SUMMARY The advent of multi‐core processors has made parallel computing techniques mandatory on mainstream systems. With the recent rise in hardware accelerators, hybrid parallelism adds yet another dimension of complexity to the process of software development. The inner workings of a parallel program are usually difficult to understand and verify. This paper presents a tool for graphical program flow analysis of hardware accelerated parallel programs. It monitors the hybrid program execution to record and visualize many performance relevant events along the way. Representative real‐world applications written for both IBM's Cell processor and NVIDIA's CUDA API are studied exemplarily. With our combined monitoring and visualization approach for hardware accelerated multi‐core and multi‐node systems we take the next step in tool evolution towards a highly improved level of detail, precision, and completeness. The contents of this paper is of interest to developers of hardware accelerated applications as well as performance tool architects. Copyright © 2011 John Wiley & Sons, Ltd.

The power of GPUs is giving rise to heterogeneous parallel computing, with new demands on programming environments, runtime systems, and tools to deliver high-performing applications. This paper studies the problems associated with performance measurement of heterogeneous machines with GPUs. A heterogeneous computation model and alternative host-GPU measurement approaches are discussed to set the stage for reporting new capabilities for heterogeneous parallel performance measurement in three leading HPC tools: PAPI, Vampir, and the TAU Performance System. Our work leverages the new CUPTI tool support in NVIDIA's CUDA device library. Heterogeneous benchmarks from the SHOC suite are used to demonstrate the measurement methods and tool support.

The advent of multi-core processors has made parallel computing techniques mandatory on main stream systems. With the recent rise of hardware accelerators, hybrid parallelism adds yet another dimension of complexity to the process of software development. This article presents a tool for graphical program flow analysis of hardware accelerated parallel programs. It monitors the hybrid program execution to record and visualize many performance relevant events along the way. Representative real-world applications written for both IBM's Cell processor and NVIDIA's CUDA API are studied exemplarily. To the best of our knowledge, this approach is the first that visualizes the parallelism in hybrid multi-core systems at the presented level of detail.