During the last few years, Graphics Processing Units (GPU) have evolved from simple devices for the display signal preparation into powerful coprocessors that do not only support typical computer graphics tasks such as rendering of 3D scenarios but can also be used for general numeric and symbolic computation tasks such as simulation and optimization. As major advantage, GPUs provide extremely ...

Any modern Graphics Processing Unit (graphics card) is a good platform to run massively parallel programs. Still, we lack tools observe and measure performance characteristics of GPU-based software. We state that due complex memory hierarchy thou- sands execution threads the all issues are about efficient use graphics card hierarchy. propose GPGPUSim simulator, previously used mostly for archit...

In program inference, the evaluation of how well a candidate solution solves a certain task is usually a computationally intensive procedure. Most of the time, the evaluation involves either submitting the program to a simulation process or testing its behavior on many input arguments; both situations may turn out to be very time-consuming. Things get worse when the optimization algorithm needs...

We show how to exploit the raw power of current graphics processing units (GPUs) to obtain implementations of SAT solving algorithms that surpass the performance of CPU-based algorithms. We have developed a GPU-based version of the survey propagation algorithm, an incomplete method capable of solving hard instances of random k-CNF problems close to the critical threshold with millions of propos...

We report on our experience with integrating and using graphics processing units (GPUs) as fast parallel floatingpoint co-processors to accelerate two fundamental computational scientific kernels on the GPU: sparse direct factorization and nonlinear interior-point optimization. Since a full re-implementation of these complex kernels is typically not feasible, we identify the matrix-matrix multi...