This workshop, to be held May 23-24 2006 in Chapel Hill, North Carolina, will address some recent developments on new commodity architectures, including GPUs, multi-core CPUs, the Cell processor, PPU and other emerging commodity architectures. Some of the issues to be examined in the workshop include the software challenges that arise in programming these new commodity architectures and their impact on different applications and high-performance computing. The workshop will bring together leading researchers and designers from academia, research labs, industrial organizations and federal agencies. A call for posters is now online. (EDGE Workshop 2006)

GPUTeraSort sorts billion-record wide-key databases using the data and task parallelism on the graphics processing unit (GPU) to perform memory-intensive and compute-intensive tasks while the CPU performs I/O and resource management. It exploits both the high-bandwidth GPU memory interface and the lower-bandwidth CPU main memory interface to achieve higher aggregate memory bandwidth than purely CPU-based algorithms. It also pipelines disk transfers to achieve near-peak I/O performance. GPUTera-Sort is a two-phase task pipeline: (1) read disk, build keys, sort using the GPU, generate runs, write disk, and (2) read, merge, write. We tested the performance of GPUTeraSort on billion-record files using the standard Sort benchmark. In practice, a 3 GHz Pentium IV PC with $265 NVIDIA 7800 GT GPU is significantly faster than optimized CPU-based algorithms on much faster processors, sorting 60GB for a penny; the best reported PennySort price-performance. These results suggest that a GPU co-processor can significantly improve performance on large data processing tasks. (GPUTeraSort: High Performance Graphics Coprocessor Sorting for Large Database Management . Naga K. Govindaraju, Jim Gray, Ritesh Kumar, and Dinesh Manocha. Proceedings of ACM SIGMOD 2006.)