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A Comparison of Many-threaded Differential Evolution and Genetic Algorithms on CUDA

Pavel Kromer, Jan Platos, Vaclav Snasel, Ajith Abraham

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Tags: Algorithms, Computer science, CUDA, Differential evolution, Genetic programming, Metaheuristics, nVidia, Task scheduling, Tesla C2050

November 2, 2011 by hgpu

An Implementation of Differential Evolution for Independent Tasks Scheduling on GPU

Pavel Kromer, Jan Platos, Vaclav Snasel, Ajith Abraham

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, Differential evolution, Heterogeneous systems, nVidia, Optimization, Task scheduling, Tesla C2050

November 1, 2011 by hgpu

Workload Balancing on Heterogeneous Systems: A Case Study of Sparse Grid Interpolation

Alin Murarasu, Josef Weidendorfer, Arndt Bode

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Tags: Compression, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, Task scheduling, Tesla C2050

October 31, 2011 by hgpu

Applying software-managed caching and CPU/GPU task scheduling for accelerating dynamic workloads

Mark Silberstein, Assaf Schuster, John D. Owens

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 285, Optimization, Package, Performance, Probability, Task scheduling, Tesla C2050

October 5, 2011 by hgpu

A New Class of Parallel Scheduling Algorithms

Wojciech Bozejko

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Tags: Algorithms, Book, Computer science, CUDA, Metaheuristics, nVidia, Optimization, Programming techniques, Task scheduling, Tesla C1060, Tesla C2050

October 2, 2011 by hgpu

PGEM: Preemptive GPGPU Execution Model for Runtime Engines

Shinpei Kato, Karthik Lakshmanan, Aman Kumar, Mihir Kelkar, Yutaka Ishikawa, Ragunathan (Raj) Rajkumar

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Package, Performance, Task scheduling

September 26, 2011 by hgpu

Dynamic Data Structures for Taskgraph Scheduling Policies with Applications in OpenCL Accelerators

Jakub Marecek, Andrew J. Parkes, Edmund K. Burke, Robert Elliot, Hedley Francis, Anton Lokhmotov

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Tags: Computer science, Data Structures and Algorithms, OpenCL, Task scheduling

September 24, 2011 by hgpu

An adaptative game loop architecture with automatic distribution of tasks between CPU and GPU

Mark Joselli, Marcelo Zamith, Esteban Clua, Anselmo Montenegro, Regina Leal-Toledo, Aura Conci, Paulo Pagliosa, Luis Valente, Bruno Feijo

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Tags: Computer science, CUDA, Games, nVidia, nVidia GeForce 8200 M, nVidia GeForce 8400 GS, nVidia GeForce 8800 GTS, Performance, Task scheduling

September 19, 2011 by hgpu

Processing data streams with hard real-time constraints on heterogeneous systems

Uri Verner, Assaf Schuster, Mark Silberstein

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Tags: AES, Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 285, Performance, Security, Task scheduling

September 15, 2011 by hgpu

TimeGraph: GPU scheduling for real-time multi-tasking environments

Shinpei Kato, Karthik Lakshmanan, Ragunathan Rajkumar, Yutaka Ishikawa

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Tags: Benchmarking, Computer science, nVidia, nVidia GeForce 9800 GT, nVidia GeForce GTX 285, nVidia GeForce GTX 480, OpenGL, Operating systems, Package, Real-time graphics, Task scheduling

September 11, 2011 by hgpu

Pegasus: coordinated scheduling for virtualized accelerator-based systems

Vishakha Gupta, Karsten Schwan, Niraj Tolia, Vanish Talwar, Parthasarathy Ranganathan

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 9800 GTX, Operating systems, Task scheduling, Virtualization

September 7, 2011 by hgpu

Improving Scheduling Techniques in Heterogeneous Systems with Dynamic, On-Line Optimisations

Marcin Bogdanski, Peter R. Lewis, Tobias Becker, Xin Yao

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Tags: Algorithms, Computer science, Finance, FPGA, Heterogeneous systems, Machine learning, Task scheduling

September 3, 2011 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

A Comparison of Many-threaded Differential Evolution and Genetic Algorithms on CUDA

An Implementation of Differential Evolution for Independent Tasks Scheduling on GPU

Workload Balancing on Heterogeneous Systems: A Case Study of Sparse Grid Interpolation

Applying software-managed caching and CPU/GPU task scheduling for accelerating dynamic workloads

A New Class of Parallel Scheduling Algorithms

PGEM: Preemptive GPGPU Execution Model for Runtime Engines

Dynamic Data Structures for Taskgraph Scheduling Policies with Applications in OpenCL Accelerators

An adaptative game loop architecture with automatic distribution of tasks between CPU and GPU

Processing data streams with hard real-time constraints on heterogeneous systems

TimeGraph: GPU scheduling for real-time multi-tasking environments

Pegasus: coordinated scheduling for virtualized accelerator-based systems

Improving Scheduling Techniques in Heterogeneous Systems with Dynamic, On-Line Optimisations

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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