Measurement techniques

hgpu.org » Measurement techniques

Verified Instruction-Level Energy Consumption Measurement for NVIDIA GPUs

Yehia Arafa, Ammar ElWazir, Abdelrahman ElKanishy, Youssef Aly, Ayatelrahman Elsayed, Abdel-Hameed Badawy, Gopinath Chennupati, Stephan Eidenbenz, Nandakishore Santhi

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Tags: Computer science, CUDA, Energy-efficient computing, Measurement techniques, nVidia, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX Titan V, nVidia GeForce GTX Titan X, nVidia Titan RTX, PTX

February 23, 2020 by hgpu

GPU performance comparison for accelerated radar data processing

Chris Fallen, Beau Bellamy, Greg Newby, Brent Watkins

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Tags: Measurement techniques, nVidia, nVidia GeForce GTX 480, OpenCL, Presentation, Signal processing, Tesla M2050

October 4, 2011 by hgpu

Improving processing time for visual measurements of displacements of IPMC actuators using CUDA

K. Tsiakmakis, T. Laopoulos

Tags: CUDA, Image processing, Materials Science, Measurement techniques, nVidia, Physics

June 26, 2011 by hgpu

Evolution of thread-level parallelism in desktop applications

Geoffrey Blake, Ronald G. Dreslinski, Trevor Mudge, Krisztian Flautner

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Tags: Benchmarking, Computer science, Measurement techniques, nVidia, nVidia GeForce GT 120, nVidia GeForce GTX 285, nVidia ION

November 7, 2010 by hgpu

cuFINUFFT: a load-balanced GPU library for general-purpose nonuniform FFTs

KTT - Kernel Tuning Toolkit

Searching CUDA code autotuning spaces with hardware performance counters: data from benchmarks running on various GPU architectures

HSA-on-FPGA

Transparent FPGA Acceleration with TensorFlow

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high performance computing on graphics processing units: hgpu.org

Verified Instruction-Level Energy Consumption Measurement for NVIDIA GPUs

GPU performance comparison for accelerated radar data processing

Improving processing time for visual measurements of displacements of IPMC actuators using CUDA

Evolution of thread-level parallelism in desktop applications

Recent source codes

GPyTorch: Gaussian processes for modern machine learning systems

Neural Network Libraries - Examples

AutoDock-GPU: AutoDock for GPUs and other accelerators

BASEMENT - Basic Simulation Environment

Charm++: a message-passing parallel language and runtime system

KTT - Kernel Tuning Toolkit

UPC++: a PGAS library for C++

cuFINUFFT: a load-balanced GPU library for general-purpose nonuniform FFTs

KTT - Kernel Tuning Toolkit

HSA-on-FPGA

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Featured events

9th International Workshop on OpenCL and SYCL, 2021

The 18th International Conference on High Performance Computing & Simulation (HPCS), 2020

The Fifth International Workshop on GPU Computing and AI (GCA), 2020

2nd International Conference on Frontiers of Intelligent Manufacturing and Automation (CFIMA’21), 2021

International Conference on Cyber Physical Systems and IoT (CPSIOT’20), 2020