hgpu.org » Operating systems
Shinpei Kato, Karthik Lakshmanan, Ragunathan Rajkumar, Yutaka Ishikawa
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
Alexander Schmidt, Andreas Polze
September 9, 2011 by hgpu
Vishakha Gupta, Rob Knauerhase, Karsten Schwan
Tags: Cloud, Computer science, Heterogeneous systems, Operating systems, Performance, Virtualization
September 9, 2011 by hgpu
Vishakha Gupta, Karsten Schwan, Niraj Tolia, Vanish Talwar, Parthasarathy Ranganathan
Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 9800 GTX, Operating systems, Task scheduling, Virtualization
September 7, 2011 by hgpu
Christopher J. Rossbach, Jon Currey, Emmett Witchel
September 7, 2011 by hgpu
Flavio Vella, Riccardo M. Cefal, Alessandro Costantini, Osvaldo Gervasi, Claudio Tanci
Tags: Cloud, Computer science, Grid, Operating systems, Virtualization
August 8, 2011 by hgpu
Shinpei Kato, Karthik Lakshmanan, Yutaka Ishikawa, Ragunathan (Raj) Rajkumar
June 22, 2011 by hgpu
Dongkyun Jeong, Kamalneet Singh, Namin Kim, Soochan Lim
May 16, 2011 by hgpu
Andrew Baumann, Paul Barham, Pierre E. Dagand, Tim Harris, Rebecca Isaacs, Simon Peter, Timothy Roscoe, Adrian Schüpbach, Akhilesh Singhania
November 27, 2010 by hgpu
Recent source codes
* * *
Most viewed papers (last 30 days)
- MusaCoder: Native GPU Kernel Generation with Full-Stack Training on Moore Threads GPU
- KForge: LLM-Driven Cross-Platform Kernel Generation for AI Accelerators
- Towards Feedback-to-Plan Decisions for Self-Evolving LLM Agents in CUDA Kernel Generation
- CodegenBench: Can LLMs Write Efficient Code Across Architectures?
- Autonomous heterogeneous catalyst discovery with a self-evolving multi-agent digital twin
- Leveraging AI Ecosystem for Portable and Sustainable GPU Kernels in HPC
- daVinci-kernel: Co-Evolving Skill Selection, Summarization, and Utilization via RL for GPU Kernel Optimization
- Tangram: Hiding GPU Heterogeneity for Efficient LLM Parallelization
- Fearless Concurrency on the GPU
- From Tokens to Regions: CUDA-Sensitive Instruction Tuning for GPU Kernel Generation
* * *




