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Deep Language Models for Software Testing and Optimisation

Foivos Tsimpourlas

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Tags: Benchmarking, Computer science, Deep learning, nVidia, nVidia GeForce GTX 1080, nVidia GeForce Titan X, OpenCL, Package, Software Engineering, Thesis

June 25, 2023 by hgpu

Compilation and Design Space Exploration of Dataflow Programs for Heterogeneous CPU-GPU Platforms

Aurélien François Gilbert Bloch

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Tags: Compilers, Computer science, CUDA, Design space exploration, DSP, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 1660, nVidia GeForce RTX 3080 Ti, Package, Thesis

June 25, 2023 by hgpu

DGEMM on Integer Matrix Multiplication Unit

Hiroyuki Ootomo, Katsuhisa Ozaki, Rio Yokota

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Tags: Computer science, CUBLAS, CUDA, Deep learning, Linear Algebra, Machine learning, Matrix multiplication, nVidia, nVidia A100, nVidia Jetson AGX Orin, nVidia RTX 6000 Ada, nVidia Titan RTX, Package

June 25, 2023 by hgpu

GPU First – Execution of Legacy CPU Codes on GPUs

Shilei Tian, Tom Scogland, Barbara Chapman, Johannes Doerfert

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Tags: Benchmarking, Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia A100, OpenMP

June 25, 2023 by hgpu

ACC Saturator: Automatic Kernel Optimization for Directive-Based GPU Code

Kazuaki Matsumura, Simon Garcia De Gonzalo, Antonio J. Peña

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Tags: Algorithms, Benchmarking, Code generation, Compilers, Computer science, nVidia, OpenACC, OpenMP, Tesla A100

June 25, 2023 by hgpu

Improving Performance of Iterative Applications through Interleaved Execution of Approximated CUDA Kernels

Gabriel Freytag

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Tags: Computer science, CUDA, Machine learning, Mixed precision, Neural networks, nVidia, nVidia A100, nVidia P100, Thesis

June 18, 2023 by hgpu

Reducing branch divergence to speed up parallel execution of unit testing on GPUs

Taghreed Bagies, Wei Le, Jeremy Sheafer, Ali Jannesari

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Tags: Benchmarking, Computer science, CUDA, nVidia, Package, Performance, Tesla V100

June 18, 2023 by hgpu

Efficient GPU implementation of a class of array permutations

Mathis Bouverot-Dupuis, Mary Sheeran

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Tags: Algorithms, AMD Radeon Instinct MI100, ATI, Computer science, CUDA, Haskell, nVidia, nVidia GeForce RTX 4090, Package

June 18, 2023 by hgpu

cuCatch: A Debugging Tool for Efficiently Catching Memory Safety Violations in CUDA Applications

Mohamed Tarek Ibn Ziad, Sana Damani, Aamer Jaleel, Stephen W. Keckler, Mark Stephenson

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce RTX 2080 Ti, OpenACC, OpenCL, Security

June 18, 2023 by hgpu

EfficientBioAI: Making Bioimaging AI Models Efficient in Energy, Latency and Representation

Yu Zhou, Justin Sonneck, Sweta Banerjee, Stefanie Dörr, Anika Grüneboom, Kristina Lorenz, Jianxu Chen

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Tags: Artificial intelligence, Biology, Computer science, CUDA, nVidia, nVidia Quadro RTX 4000, Package

June 18, 2023 by hgpu

GPUHarbor: Testing GPU Memory Consistency at Large

Reese Levine, Mingun Cho, Devon McKee, Andrew Quinn, Tyler Sorensen

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Tags: AMD Radeon Pro 5500M, ATI, Computer science, Memory model, nVidia, nVidia GeForce RTX 3080, nVidia Tegra TX1, OpenCL, Performance

June 11, 2023 by hgpu

Program Analysis and Machine Learning based Approach to Predict Power Consumption of CUDA Kernel

Gargi Alavani, Jineet Desai, Snehanshu Saha, Santonu Sarkar

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Tags: Benchmarking, Computer science, CUDA, Energy-efficient computing, Machine learning, nVidia, Package, Tesla K20, Tesla M60, Tesla V100

June 11, 2023 by hgpu

Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations

SW#SYCL

Analyzing the Performance Portability of SYCL across CPUs, GPUs, and Hybrid Systems with Protein Database Search

tdg-benchs: benchmarks used to test the performance of taskgraph

Leveraging the potential of task-based programming with OpenMP task graphs

LLOR: Automatic Repair of OpenMP Programs

LLOR: Automated Repair of OpenMP Programs

Solomon: Simple Off-LOading Macros Orchestrating multiple Notations

Unified schemes for directive-based GPU offloading

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

PyOMP: Parallel programming for CPUs and GPUs with OpenMP and Python

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high performance computing on graphics processing units: hgpu.org

Applications

Deep Language Models for Software Testing and Optimisation

Compilation and Design Space Exploration of Dataflow Programs for Heterogeneous CPU-GPU Platforms

DGEMM on Integer Matrix Multiplication Unit

GPU First – Execution of Legacy CPU Codes on GPUs

ACC Saturator: Automatic Kernel Optimization for Directive-Based GPU Code

Improving Performance of Iterative Applications through Interleaved Execution of Approximated CUDA Kernels

Reducing branch divergence to speed up parallel execution of unit testing on GPUs

Efficient GPU implementation of a class of array permutations

cuCatch: A Debugging Tool for Efficiently Catching Memory Safety Violations in CUDA Applications

EfficientBioAI: Making Bioimaging AI Models Efficient in Energy, Latency and Representation

GPUHarbor: Testing GPU Memory Consistency at Large

Program Analysis and Machine Learning based Approach to Predict Power Consumption of CUDA Kernel

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Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations

SW#SYCL

tdg-benchs: benchmarks used to test the performance of taskgraph

LLOR: Automatic Repair of OpenMP Programs

Solomon: Simple Off-LOading Macros Orchestrating multiple Notations

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

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