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Automated Architecture Design for Deep Neural Networks

Steven Abreu

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Tags: Computer science, Deep learning, GPU cluster, Machine learning, Neural networks, nVidia, Tesla K80, Thesis

September 1, 2019 by hgpu

Survey and Benchmarking of Machine Learning Accelerators

Albert Reuther, Peter Michaleas, Michael Jones, Vijay Gadepally, Siddharth Samsi, Jeremy Kepner

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Tags: ASIC, Benchmarking, Computer science, CUDA, FPGA, Machine learning, Neural networks, nVidia, nVidia Jetson TX1, nVidia Jetson TX2, Performance, TPU

September 1, 2019 by hgpu

Position-Dependent Arrays and Their Application for High Performance Code Generation

Federico Pizzuti, Michel Steuwer, Christophe Dubach

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Tags: Code generation, Computer science, nVidia, nVidia GeForce GTX Titan X, OpenCL

August 25, 2019 by hgpu

stdgpu: Efficient STL-like Data Structures on the GPU

Patrick Stotko

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Tags: Computer science, CUDA, Data Structures and Algorithms, nVidia, Package

August 25, 2019 by hgpu

Automatic Compiler Based FPGA Accelerator for CNN Training

Shreyas Kolala Venkataramanaiah, Yufei Ma, Shihui Yin, Eriko Nurvithadhi, Aravind Dasu, Yu Cao, Jae-sun Seo

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Tags: Code generation, Compilers, Computer science, Deep learning, FPGA, Neural networks

August 25, 2019 by hgpu

On-The-Fly Parallel Data Shuffling for Graph Processing on OpenCL-based FPGAs

Xinyu Chen, Ronak Bajaj, Yao Chen, Jiong He, Bingsheng He, Weng-Fai Wong, Deming Chen

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Tags: Computer science, FPGA, Graph theory, OpenCL

August 25, 2019 by hgpu

Memory-Efficient Object-Oriented Programming on GPUs

Matthias Springer

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 940 M, nVidia GeForce GTX 980, nVidia GeForce GTX Titan XP, Package, Programming Languages, Thesis

August 25, 2019 by hgpu

Survey paper on Deep Learning on GPUs

Sparsh Mittal, Shraiysh Vaishay

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Tags: Deep learning, Paper, Research, survey

August 21, 2019 by sparsh0mittal

Mass Estimation from Images using Deep Neural Network and Sparse Ground Truth

Muhammad K.A. Hamdan, Diane Rover, John Just

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Tags: Computer science, Deep learning, Machine learning, Neural networks, Package

August 18, 2019 by hgpu

Efficient Simulation of Fluid Flow and Transport in Heterogeneous Media Using Graphics Processing Units (GPUs)

Hassan Dashtian, Muhammad Sahimi

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Tags: Computational Physics, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 880, Physics

August 18, 2019 by hgpu

High Performance Computing via High Level Synthesis

Mehdi Roozmeh

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Tags: Computer science, Design space exploration, FPGA, Heterogeneous systems, HLS, Nearest neighbour, nVidia, OpenCL, Package, Signal processing, Thesis

August 18, 2019 by hgpu

Visual Analysis Algorithms for Embedded Systems

Syed Tahir Hussain Rizvi

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia Jetson K1, nVidia Jetson TX1, Thesis

August 18, 2019 by hgpu

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

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

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

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

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

GEAK-agent: LLM-based AI agent, which can write correct and efficient GPU kernels automatically

Geak: Introducing Triton Kernel AI Agent & Evaluation Benchmarks

OpenDwarfs 2025: re-engineered version of the OpenDwarfs benchmark suite, for compatibility with modern platforms

OpenDwarfs 2025: Modernizing the OpenDwarfs Benchmark Suite for Heterogeneous Computing

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high performance computing on graphics processing units: hgpu.org

Applications

Automated Architecture Design for Deep Neural Networks

Survey and Benchmarking of Machine Learning Accelerators

Position-Dependent Arrays and Their Application for High Performance Code Generation

stdgpu: Efficient STL-like Data Structures on the GPU

Automatic Compiler Based FPGA Accelerator for CNN Training

On-The-Fly Parallel Data Shuffling for Graph Processing on OpenCL-based FPGAs

Memory-Efficient Object-Oriented Programming on GPUs

Survey paper on Deep Learning on GPUs

Mass Estimation from Images using Deep Neural Network and Sparse Ground Truth

Efficient Simulation of Fluid Flow and Transport in Heterogeneous Media Using Graphics Processing Units (GPUs)

High Performance Computing via High Level Synthesis

Visual Analysis Algorithms for Embedded Systems

Recent source codes

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)

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

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

SIGMo: Scalable Isomorphism Graph Matching on GPUs

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

GEAK-agent: LLM-based AI agent, which can write correct and efficient GPU kernels automatically

OpenDwarfs 2025: re-engineered version of the OpenDwarfs benchmark suite, for compatibility with modern platforms

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