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High performance multi-GPU computing becomes an inevitable trend due to the ever-increasing demand on computation capability in emerging domains such as deep learning, big data and planet-scale simulations. However, the lack of deep understanding on how modern GPUs can be connected and the real impact of state-of-the-art interconnect technology on multi-GPU application performance become a hurdle. In this paper, we fill the gap by conducting a thorough evaluation on five latest types of modern GPU interconnects: PCIe, NVLink-V1, NVLink-V2, NVLink-SLI and NVSwitch, from six high-end servers and HPC platforms: NVIDIA P100-DGX-1, V100-DGX-1, DGX-2, OLCF's SummitDev and Summit supercomputers, as well as an SLI-linked system with two NVIDIA Turing RTX-2080 GPUs. Based on the empirical evaluation, we have observed four new types of GPU communication network NUMA effects: three are triggered by NVLink's topology, connectivity and routing, while one is caused by PCIe chipset design issue. These observations indicate that, for an application running in a multi-GPU node, choosing the right GPU combination can impose considerable impact on GPU communication efficiency, as well as the application's overall performance. Our evaluation can be leveraged in building practical multi-GPU performance models, which are vital for GPU task allocation, scheduling and migration in a shared environment (e.g., AI cloud and HPC centers), as well as communication-oriented performance tuning. [ABSTRACT FROM AUTHOR]

IBMs Blue Gene supercomputer architecture has evolved through three successive generations each providing increased levels of power-efficiency and system densities. From the original Blue Gene/L to P to Q, a higher level of integration has enabled higher single-core performance, larger concurrency per compute node, and a higher level of system integration. Although these changes have brought with them a higher overall system peak-performance, no study has examined in detail the evolution of performance across system generations. In this work we make two significant contributions that of providing a comparative performance analysis across Blue Gene generations using a consistent set of tests, and also in providing a validated performance model of the NEK-Bone proxy application from the DOE CESAR Exascale Co-Design Center. The combination of empirical analysis and the predictive capabilities of the NEK-Bone performance model enable us to not only directly compare measured performance but also allow for a comparison of system configurations that cannot currently be measured. We provide insights into how the changing architectural performance characteristics of Blue Gene have impacted on the application performance, as well as providing insight into what future systems may be able to achieve. [ABSTRACT FROM AUTHOR]

Direct Numerical Simulation (DNS) is an important application area that is expected to use large fractions of future large-scale simulations. In this work we develop, validate and use a performance model of the combustion code, DNS3D, to explore achieved performance on current parallel systems. The performance model is developed from a thorough analysis of the application. Its key computation characteristics are coupled with the performance characteristics of the system using an parameterized analytical model. The model is validated on three parallel systems: a muti-core AMD Opteron based system with an Infiniband fat-tree network, an IBM Power5+ system with an HPS fat-tree network, and an IBM Power7 system with a direct connect network. The performance model is shown to achieve high prediction accuracy on all three systems. We illustrate how the model can be used to explore impact of changes in either the system or the application. It is used to both analyze the achieved performance on these systems as well as to explore the possible benefits of further optimizing DNS3D's main computational kernel of one-dimensional FFTs, or in possibly overlapping communication with computation. [ABSTRACT FROM AUTHOR]

The article focuses on the use of computer performance modeling to design ultrascale systems from start to actual use. It is stated that the method offers analytical formulas that describe the performance of an application running on a current supercomputer and that it supports prediction, insight and interpretation. Performance modeling has also reportedly been used to quantify systems before implementing optimization and can be integrated into a system's everyday operation.