Recent advances in GPUs (graphics processing units) lead to massively parallel hardware that is easily programmable and widely applied in areas which require intensive computation besides graphics acceleration. The appearance of GPU clusters gains popularity in the scientific computing community, and the study on GPU clusters becomes an increasingly hot issue. While extending a singleGPU system...

Methods to implement stochastic simulations on the Graphics Processing Unit (GPU) have been developed. These algorithms are used in a simulation of micro and nano assembly with optical tweezers, but are also directly compatible with simulations of a wide variety of assembly techniques using either electrophoretic, magnetic or other trapping techniques. Significant speedup is possible for stocha...

Dynamic tainting tracks the influence of certain inputs (taint sources) through execution and it is a powerful tool for information flow analysis and security. Taint tracking has primarily targeted CPU program executions. Motivated by recent recognition of information leaking in GPU memory and GPU-resident malware, this paper presents the first design and prototype implementation of a taint tra...

GPU parallelism for real applications can achieve enormous performance gain. CPU-GPU Communication is one of the major bottlenecks that limit this performance gain. Among several libraries developed so far to optimize this communication, DyManD (Dynamically Managed Data) provides better communication optimization strategies and achieves better performance on a single GPU. SmithWaterman is a wel...

| The graphics processing unit (GPU) has become an integral part of today’s mainstream computing systems. Over the past six years, there has been a marked increase in the performance and capabilities of GPUs. The modern GPU is not only a powerful graphics engine but also a highly parallel programmable processor featuring peak arithmetic and memory bandwidth that substantially outpaces its CPU c...

General purpose GPUs are a new and powerful hardware device with a number of applications in the realm of relational databases. We describe a database framework designed to allow both CPU and GPU execution of queries. Through use of our novel data structure design and method of using GPUmapped memory with efficient caching, we demonstrate that GPU query acceleration is possible for data sets mu...

As data sets grow and conventional processor performance scaling slows, data analytics move towards heterogeneous architectures that incorporate hardware accelerators (notably GPUs) to continue scaling performance. However, existing GPU-based databases fail to deal with big data applications efficiently: their execution model suffers from scalability limitations on GPUs whose memory capacity is...