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Evaluation of Intel’s DPC++ Compatibility Tool in heterogeneous computing

Germán Castaño, Youssef Faqir-Rhazoui, Carlos García, Manuel Prieto-Matías

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, Package, Performance, SYCL

July 3, 2022 by hgpu

Deep Learning Models on CPUs: A Methodology for Efficient Training

Quchen Fu, Ramesh Chukka, Keith Achorn, Thomas Atta-fosu, Deepak R. Canchi, Zhongwei Teng, Jules White, Douglas C. Schmidt

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Tags: Computer science, Deep learning, Performance, PyTorch, Visualization

June 26, 2022 by hgpu

CoDL: Efficient CPU-GPU Co-execution for Deep Learning Inference on Mobile Devices

Fucheng Jia, Deyu Zhang, Ting Cao, Shiqi Jiang, Yunxin Liu, Ju Ren, Yaoxue Zhang

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Tags: Computer science, Deep learning, Heterogeneous systems, Package, Performance

June 19, 2022 by hgpu

Modeling GPU Dynamic Parallelism for Self Similar Density Workloads

Felipe A. Quezada, Cristóbal A. Navarro, Miguel Romero, Cristhian Aguilera

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Tags: Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce Titan V, nVidia Jetson NX, nVidia Titan RTX, Performance

June 12, 2022 by hgpu

On the Compilation Performance of Current SYCL Implementations

Peter Thoman, Facundo Molina Heredia, Thomas Fahringer

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Tags: Code generation, Compilers, Computer science, Heterogeneous systems, OpenCL, Package, Performance, SYCL

June 12, 2022 by hgpu

Dissecting Tensor Cores via Microbenchmarks: Latency, Throughput and Numerical Behaviors

Wei Sun, Ang Li, Tong Geng, Sander Stuijk, Henk Corporaal

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Tags: Benchmarking, Computer science, Matrix multiplication, nVidia, nVidia A100, nVidia GeForce RTX 2080, nVidia GeForce RTX 2080 Ti, Performance, PTX, Sparse matrix

June 12, 2022 by hgpu

Onesweep: A Faster Least Significant Digit Radix Sort for GPUs

Andy Adinets, Duane Merrill

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia A100, Package, Performance, Sorting

June 12, 2022 by hgpu

SOL: Reducing the Maintenance Overhead for Integrating Hardware Support into AI Frameworks

Nicolas Weber

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Tags: AI, Artificial intelligence, Computer science, CUDA, Machine learning, Neural networks, nVidia, nVidia GeForce RTX 2080, Performance

May 29, 2022 by hgpu

Experience of Migrating a Parallel Graph Coloring Program from CUDA to SYCL

Zheming Jin

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Tags: Benchmarking, Computer science, CUDA, Graph theory, nVidia, OpenCL, Package, Performance, SYCL

May 8, 2022 by hgpu

GPUNet: Searching the Deployable Convolution Neural Networks for GPUs

Linnan Wang, Chenhan Yu, Satish Salian, Slawomir Kierat, Szymon Migacz, Alex Fit Florea

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia Quadro GV100, Performance, Tesla A100

May 8, 2022 by hgpu

Improving performance of SYCL applications on CPU architectures using LLVM-directed compilation flow

Pietro Ghiglio, Uwe Dolinsky, Mehdi Goli, Kumudha Narasimhan

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Tags: Computer science, CUDA, nVidia, OpenCL, Performance, performance portability, SYCL

May 1, 2022 by hgpu

Performance Comparison of Different OpenCL Implementations of LBM Simulation on Commodity Computer Hardware

Jelena Tekic, Predrag Tekic, Milos Rackovic

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Tags: AMD Radeon R5 M420, AMD Radeon R5 M430, ATI, cfd, Fluid dynamics, Heterogeneous systems, Lattice Boltzmann model, nVidia, nVidia GeForce GTX 940, nVidia GeForce GTX 960, OpenCL, Performance, Tesla C2070

April 17, 2022 by hgpu

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Efficient deep learning inference on end devices

Ouroboros: Virtualized Queues for dynamic memory management

Dynamic Memory Management on GPUs with SYCL

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high performance computing on graphics processing units: hgpu.org

Evaluation of Intel’s DPC++ Compatibility Tool in heterogeneous computing

Deep Learning Models on CPUs: A Methodology for Efficient Training

CoDL: Efficient CPU-GPU Co-execution for Deep Learning Inference on Mobile Devices

Modeling GPU Dynamic Parallelism for Self Similar Density Workloads

On the Compilation Performance of Current SYCL Implementations

Dissecting Tensor Cores via Microbenchmarks: Latency, Throughput and Numerical Behaviors

Onesweep: A Faster Least Significant Digit Radix Sort for GPUs

SOL: Reducing the Maintenance Overhead for Integrating Hardware Support into AI Frameworks

Experience of Migrating a Parallel Graph Coloring Program from CUDA to SYCL

GPUNet: Searching the Deployable Convolution Neural Networks for GPUs

Improving performance of SYCL applications on CPU architectures using LLVM-directed compilation flow

Performance Comparison of Different OpenCL Implementations of LBM Simulation on Commodity Computer Hardware

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Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

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Ouroboros: Virtualized Queues for dynamic memory management

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