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Literature Review and Implementation Overview: High Performance Computing with Graphics Processing Units for Classroom and Research Use
In this report, I discuss the history and current state of GPU HPC syste...
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Double-precision FPUs in High-Performance Computing: an Embarrassment of Riches?
Among the (uncontended) common wisdom in High-Performance Computing (HPC...
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Memory-Efficient Object-Oriented Programming on GPUs
Object-oriented programming is often regarded as too inefficient for hig...
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On the Efficient Evaluation of the Exchange Correlation Potential on Graphics Processing Unit Clusters
The predominance of Kohn-Sham density functional theory (KS-DFT) for the...
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High-Performance Statistical Computing in the Computing Environments of the 2020s
Technological advances in the past decade, hardware and software alike, ...
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A Workload Analysis of NSF's Innovative HPC Resources Using XDMoD
Workload characterization is an integral part of performance analysis of...
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Time-Based Roofline for Deep Learning Performance Analysis
Deep learning applications are usually very compute-intensive and requir...
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8 Steps to 3.7 TFLOP/s on NVIDIA V100 GPU: Roofline Analysis and Other Tricks
Performance optimization can be a daunting task especially as the hardware architecture becomes more and more complex. This paper takes a kernel from the Materials Science code BerkeleyGW, and demonstrates a few performance analysis and optimization techniques. Despite challenges such as high register usage, low occupancy, complex data access patterns, and the existence of several long-latency instructions, we have achieved 3.7 TFLOP/s of double-precision performance on an NVIDIA V100 GPU, with 8 optimization steps. This is 55 the theoretical peak, 6.7 TFLOP/s, at nominal frequency 1312 MHz, and 70 the more customized peak based on our 58 techniques used to analyze this OpenACC kernel and optimize its performance are shown, including the use of hierarchical Roofline performance model and the performance tool Nsight Compute. This kernel exhibits computational characteristics that are commonly seen in many high-performance computing (HPC) applications, and are expected to be very helpful to a general audience of HPC developers and computational scientists, as they pursue more performance on NVIDIA GPUs.
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