We present the GPU Module of a Performance Prediction Toolkit developed at Los Alamos National Laboratory, which enables code developers to efficiently test novel algorithmic ideas particularly for large-scale computational physics codes. The GPU Module is a heavily-parameterized model of the GPU hardware that takes as input a sequence of abstracted instructions that the user provides as a repr...

Recent advances in the speed and programmability of consumer level graphics hardware has sparked a flurry of research that goes beyond the realm of image synthesis and computer graphics. We examine the use of the GPU (graphics processing unit) as a tool for scientific computing, by analyzing techniques for performing large matrix multiplies in GPU hardware. An earlier method for multiplying mat...

An algorithm for performing online clustering on the GPU is proposed which makes heavy use of the atomic operations available on the GPU. The algorithm can cluster multiple documents in parallel in way that can saturate all the parallel threads on the GPU. The algorithm takes advantage of atomic operations available on the GPU in order to cluster multiple documents at the same time. The algorit...

In this work, we show that consumer-level $100 GPU can be used to significantly speed-up optimization of 0/1 Knapsack problem. We identify strong and weak points of GPU architecture and propose our parallel genetic algorithm model implemented in CUDA running entirely on the GPU. We show that GPU must be utilized for sufficiently long time in order to obtain reasonable program speedup. Then we c...

This talk presents work on transforming SQL-IMDB, a commercial available inmemory database system, into a massive parallel, array structured data processor extending the “classic” query engine architecture with GPU based co-processing facilities. The chosen approach is not just a simple re-implementation of common database functionality like sorting, stream processing and joins on GPUs, instead...

Monte Carlo Tree Search (MCTS) is a method for making optimal decisions in artificial intelligence (AI) problems, typically move planning in combinatorial games. It combines the generality of random simulation with the precision of tree search. It can theoretically be applied to any domain that can be described in terms of state, action pairs and simulation used to forecast outcomes such as dec...

We present the newly developed code, GAMER (GPU-accelerated Adaptive MEsh Refinement code), which has adopted a novel approach to improve the performance of adaptive mesh refinement (AMR) astrophysical simulations by a large factor with the use of the graphic processing unit (GPU). The AMR implementation is based on a hierarchy of grid patches with an oct-tree data structure. We adopt a three-d...

Graphics processing unit (GPU), although a powerful performance-booster, also has many security vulnerabilities. Due to these, the GPU can act as a safe-haven for stealthy malware and the weakest ‘link’ in the security ‘chain’. In this paper, we present a survey of techniques for analyzing and improving GPU security. We classify the works on key attributes to highlight their similarities and di...

An exciting development in the computing industry has been the emergence of graphics processing units (the GPU) as a fast general purpose co-processor. Initially designed for gaming applications, todays GPUs demonstrate impressive computing power and high levels of parallelism and are now being used for a variety of applications far removed from traditional graphics rendering settings. Perhaps ...