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

ProGraML: Graph-based Deep Learning for Program Optimization and Analysis

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

Optimizing Streaming Parallelism on Heterogeneous Many-Core Architectures

Learnergy: a Python library for energy-based machine learning models

Learnergy: Energy-based Machine Learners

Towards automated kernel selection in machine learning systems: A SYCL case study

CLFuzz

Automated test generation for OpenCL kernels using fuzzing and constraint solving

High-Performance Graph Primitives on GPUs

Fast Gunrock Subgraph Matching (GSM) on GPUs

Inline Vector Compression for Computational Physics

AvA: Automatic virtualization of (general) accelerators

AvA: Accelerated Virtualization of Accelerators

GPGPU Verification of Radix Sort

Let’s sort this out: GPGPU Verification of Radix Sort

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2020

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Munich, Germany

8th International Workshop on OpenCL, including SYCLCon, 2019

2019

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13-15

Boston, USA

7th International Workshop on OpenCL, 2019

2019

May

Boston, MA, USA

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

2019

May

Larnaca, Cyprus

3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Neural Networks (ICFNN), 2019

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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

ProGraML: Graph-based Deep Learning for Program Optimization and Analysis

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

Optimizing Streaming Parallelism on Heterogeneous Many-Core Architectures

Learnergy: a Python library for energy-based machine learning models

Towards automated kernel selection in machine learning systems: A SYCL case study

CLFuzz

High-Performance Graph Primitives on GPUs

Inline Vector Compression for Computational Physics

AvA: Automatic virtualization of (general) accelerators

GPGPU Verification of Radix Sort

Most viewed papers (last 30 days)

Featured events

8th International Workshop on OpenCL, including SYCLCon, 2019

7th International Workshop on OpenCL, 2019

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019

International Conference on Frontiers of Neural Networks (ICFNN), 2019