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Implementation of Frequency Domain Convolution for the Caffe-Framework

Philipp Harzig

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Tags: Caffe, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, Thesis

October 8, 2016 by hgpu

GPU Concurrency Choices in Graph Analytics

Masab Ahmad, Omer Khan

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Tags: Algorithms, Computer science, Graph theory, nVidia, nVidia GeForce GTX 750 Ti, nVidia GeForce GTX 970, OpenCL

October 8, 2016 by hgpu

BioEM: GPU-accelerated computing of Bayesian inference of electron microscopy images

Pilar Cossio, David Rohr, Fabio Baruffa, Markus Rampp, Volker Lindenstruth, Gerhard Hummer

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Tags: Bayesian, Biology, Biomolecules, CUDA, Electron microscopy, Heterogeneous systems, Medicine, Microscopy, MPI, nVidia, nVidia GeForce GTX Titan, OpenMP, Package, Tesla K20, Tomography

October 8, 2016 by hgpu

Rinnegan: Efficient Resource Use in Heterogeneous Architectures

Sankaralingam Panneerselvam, Michael Swift

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Tags: Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 650, nVidia GeForce GTX 670, OpenCL

October 8, 2016 by hgpu

Efficient CSR-Based Sparse Matrix-Vector Multiplication on GPU

Jiaquan Gao, Panpan Qi, Guixia He

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, Sparse matrix, Tesla C2050, Tesla K20

October 4, 2016 by hgpu

APL on GPUs: A TAIL from the Past, Scribbled in Futhark

Troels Henriksen, Martin Dybdal, Henrik Urms, Anna Sofie Kiehn, Daniel Gavin, Hjalte Abelskov, Martin Elsman, Cosmin Oancea

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Tags: Compilers, Computer science, nVidia, nVidia GeForce GTX 780 Ti, OpenCL, Package, PyOpenCL, Python, Visualization

October 4, 2016 by hgpu

Training a Feedback Loop for Hand Pose Estimation

Markus Oberweger, Paul Wohlhart, Vincent Lepetit

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Tags: Computer science, Computer vision, CUDA, Neural networks, nVidia, nVidia GeForce GTX 780 Ti, Package, Theano

October 4, 2016 by hgpu

GPU-based timetable generation

Ricardo Filipe Pereira Neves

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Tags: Computer science, CUDA, Genetic algorithm, nVidia, nVidia GeForce GTX 770, Thesis

September 30, 2016 by hgpu

Distributed Training of Deep Neuronal Networks: Theoretical and Practical Limits of Parallel Scalability

Janis Keuper

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Tags: Caffe, Computer science, CUDA, Deep learning, Intel Xeon Phi, Neural networks, nVidia, Tesla K80

September 30, 2016 by hgpu

Combining Belief Propagation and Successive Cancellation List Decoding of Polar Codes on a GPU Platform

Sebastian Cammerer, Benedikt Leible, Matthias Stahl, Jakob Hoydis, Stephan ten Brink

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 980 Ti, Signal processing

September 30, 2016 by hgpu

Efficient and portable acceleration of quantum chemical many-body methods in mixed floating point precision using OpenACC compiler directives

Janus Juul Eriksen

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Tags: Chemical Physics, Chemistry, Mixed precision, nVidia, OpenACC, Physics, Tesla K40

September 27, 2016 by hgpu

Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations

Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 750, Package, Python

September 27, 2016 by hgpu

AutoDock-GPU: AutoDock for GPUs and other accelerators

Architecting Tensor Core-Based Reductions for Irregular Molecular Docking Kernels

NCCLX: collective communication framework

Collective Communication for 100k+ GPUs

Tutoring LLM into a Better CUDA Optimizer

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

A Performance Portable Matrix Free Dense MTTKRP in GenTen

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

ConCuR: Conciseness Makes State-of-the-Art Kernel Generation

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high performance computing on graphics processing units: hgpu.org

Implementation of Frequency Domain Convolution for the Caffe-Framework

GPU Concurrency Choices in Graph Analytics

BioEM: GPU-accelerated computing of Bayesian inference of electron microscopy images

Rinnegan: Efficient Resource Use in Heterogeneous Architectures

Efficient CSR-Based Sparse Matrix-Vector Multiplication on GPU

APL on GPUs: A TAIL from the Past, Scribbled in Futhark

Training a Feedback Loop for Hand Pose Estimation

GPU-based timetable generation

Distributed Training of Deep Neuronal Networks: Theoretical and Practical Limits of Parallel Scalability

Combining Belief Propagation and Successive Cancellation List Decoding of Polar Codes on a GPU Platform

Efficient and portable acceleration of quantum chemical many-body methods in mixed floating point precision using OpenACC compiler directives

Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations

Recent source codes

AutoDock-GPU: AutoDock for GPUs and other accelerators

NCCLX: collective communication framework

Tutoring LLM into a Better CUDA Optimizer

Kernel Library for LLM Serving

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

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