Abstract: "Recent advances in graphics processing units (GPUs) have resulted in massively parallel hardware that is easily programmable and widely available in commodity desktop computer systems. GPUs typically use single-instruction, multiple-data (SIMD) pipelines to achieve high performance with minimal overhead incurred by control hardware. Scalar threads are grouped together into SIMD batches, sometimes referred to as warps. While SIMD is ideally suited for simple programs, recent GPUs include control flow instructions in the GPU instruction set architecture and programs using these instructions may experience reduced performance due to the way branch execution is supported by hardware. One approach is to add a stack to allow different SIMD processing elements to execute distinct program paths after a branch instruction. The occurrence of diverging branch outcomes for different processing elements significantly degrades performance. In this paper, we explore mechanisms for more efficient SIMD branch execution on GPUs. We show that a realistic hardware implementation that dynamically regroups threads into new warps on the fly following the occurrence of diverging branch outcomes improves performance by an average of 20.7% for an estimated area increase of 4.7%. (Wilson W. L. Fung, Ivan Sham, George Yuan, and Tor M. Aamodt, Dynamic Warp Formation and Scheduling for Efficient GPU Control Flow, to appear in 40th IEEE/ACM International Symposium on Microarchitecture (), Chicago, IL, December 1-5, 2007.

Abstract: "N-body algorithms are applicable to a number of common problems in computational physics including gravitation, electrostatics, and fluid dynamics. Fast algorithms (those with better than O(N²) performance) exist, but have not been successfully implemented on GPU hardware for practical problems. In the present work, we introduce not only best-in-class performance for a multipole-accelerated treecode method, but a series of improvements that support implementation of this solver on highly-data-parallel graphics processing units (GPUs). The greatly reduced computation times suggest that this problem is ideally suited for the current and next generations of single and cluster CPU-GPU architectures. We believe that this is an ideal method for practical computation of largescale turbulent flows on future supercomputing hardware using parallel vortex particle methods. (Mark J. Stock and Adrin Gharakhani, "Toward efficient GPU-accelerated N-body simulations," in 46th AIAA Aerospace Sciences Meeting and Exhibit, AIAA 2008-608, January 2008, Reno, Nevada.)

Abstract: "The porting of two- and three-dimensional Euler solvers from a conventional CPU implementation to the novel target platform of the Graphics Processing Unit (GPU) is described. The motivation for such an effort is the impressive performance that GPUs offer: typically 10 times more floating point operations per second than a modern CPU, with over 100 processing cores and all at a very modest financial cost. Both codes were found to generate the same results on the GPU as the FORTRAN versions did on the CPU. The 2D solver ran up to 29 times quicker on the GPU than on the CPU; the 3D solver 16 times faster." (Tobias Brandvik and Graham Pullan, Acceleration of a 3D Euler Solver Using Commodity Graphics Hardware. 46th AIAA Aerospace Sciences Meeting and Exhibit. January, 2008.)