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Manas: Mining Software Repositories to Assist AutoML

Giang Nguyen, Johir Islam, Rangeet Pan, Hridesh Rajan

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Tags: Computer science, Deep learning, Neural networks, nVidia, Package, Python, Software Engineering, Tesla V100

December 12, 2021 by hgpu

CitiusSynapse: A Deep Learning Framework for Embedded Systems

Seungtae Hong, Hyunwoo Cho, Jeong-Si Kim

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Tags: Computer science, Deep learning, Embedded high-performance computing, OpenCL

December 12, 2021 by hgpu

High performance computing on Android devices – a case study

Robert Fritze, Claudia Plant

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Tags: AMD Radeon VII, Android, ATI, Benchmarking, Computer science, HPC, nVidia GeForce Titan V, OpenCL

December 12, 2021 by hgpu

GPU backed Data Mining on Android Devices

Robert Fritze, Claudia Plant

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Tags: Android, Computer science, Data mining, HPC, Machine learning, OpenCL, Package

December 12, 2021 by hgpu

Analysis and Comparison of Performance and Power Consumption of Neural Networks on CPU, GPU, TPU and FPGA

Christopher Noel Hesse

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Tags: Benchmarking, Computer science, Deep learning, FPGA, Neural networks, nVidia, nVidia DGX-1, nVidia GeForce RTX 2070, nVidia Jetson Nano, Performance, Tesla V100

December 5, 2021 by hgpu

Autotuning CUDA: Applying NLP Techniques to LS-CAT

Lars Bjertnes, Jacob O. Tørring, Anne C. Elster

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Tags: Auto-Tuning, Computer science, CUDA, Machine learning, NLP, nVidia

December 5, 2021 by hgpu

Bayesian Optimization for auto-tuning GPU kernels

Floris-Jan Willemsen, Rob van Nieuwpoort, Ben van Werkhoven

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Tags: Bayesian, Computer science, CUDA, Machine learning, nVidia, nVidia A100, nVidia GeForce GTX Titan X, nVidia GeForce RTX 2070, OpenCL, Package, Performance

December 5, 2021 by hgpu

Atos: A Task-Parallel GPU Dynamic Scheduling Framework for Dynamic Irregular Computations

Yuxin Chen, Benjamin Brock, Serban Porumbescu, Aydın Buluç, Katherine Yelick, John D. Owens

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Tags: Computer science, CUDA, nVidia, Task scheduling, Tesla V100

December 5, 2021 by hgpu

An Auto-Programming Approach to Vulkan

Vladimir Frolov, Vadim Sanzharov, Vladimir Galaktionov, Alexandr Scherbakov

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Tags: 3D Graphics and Realism, Code generation, nVidia, nVidia GeForce RTX 2080, Raytracing, Vulkan

December 5, 2021 by hgpu

Generating GPU Compiler Heuristics using Reinforcement Learning

Ian Colbert, Jake Daly, Norm Rubin

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Tags: AMD Radeon 6800 XT, AMD Radeon RX 5700 XT, ATI, Compilers, Computer science, Machine learning, Performance

November 28, 2021 by hgpu

Concurrency Mapping to FPGAs with OpenCL: A Case Study with a Shallow Water Kernel

Moteb Alghamdi, Graham Riley, Mike Ashworth

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Tags: Computer science, FPGA, HLS, HPC, OpenCL

November 28, 2021 by hgpu

Predictive Data Race Detection for GPUs

Sagnik Dey, Mayant Mukul, Parth Sharma, Swarnendu Biswas

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia Quadro RTX 5000, Performance

November 28, 2021 by hgpu

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Accelerating a Linear Programming Algorithm on AMD GPUs

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Towards Efficient and Practical GPU Multitasking in the Era of LLM

Bandicoot: C++ library for GPU accelerated linear algebra

Bandicoot: A Templated C++ Library for GPU Linear Algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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high performance computing on graphics processing units: hgpu.org

Applications

Manas: Mining Software Repositories to Assist AutoML

CitiusSynapse: A Deep Learning Framework for Embedded Systems

High performance computing on Android devices – a case study

GPU backed Data Mining on Android Devices

Analysis and Comparison of Performance and Power Consumption of Neural Networks on CPU, GPU, TPU and FPGA

Autotuning CUDA: Applying NLP Techniques to LS-CAT

Bayesian Optimization for auto-tuning GPU kernels

Atos: A Task-Parallel GPU Dynamic Scheduling Framework for Dynamic Irregular Computations

An Auto-Programming Approach to Vulkan

Generating GPU Compiler Heuristics using Reinforcement Learning

Concurrency Mapping to FPGAs with OpenCL: A Case Study with a Shallow Water Kernel

Predictive Data Race Detection for GPUs

Recent source codes

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Bandicoot: C++ library for GPU accelerated linear algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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