This chapter describes the first CUDA implementation of the classical Barnes Hut n-body algorithm that runs entirely on the GPU. Unlike most other CUDA programs, our code builds an irregular treebased data structure and performs complex traversals on it. It consists of six GPU kernels. The kernels are optimized to minimize memory accesses and thread divergence and are fully parallelized within ...

We extend an off-the-shelf, executable formal semantics of C (Ellison and Ros,u’s K Framework semantics) with the core features of CUDA-C. The hybrid CPU/GPU computation model of CUDA-C presents challenges not just for programmers, but also for practitioners of formal methods. Our formal semantics helps expose and clarify these issues. We demonstrate the usefulness of our semantics by generatin...

For the last decade Graphics Processor Units (GPUs) have made a great advance and became fully programmable processors. Since pixel shader 2.0 appeared, ray-tracing community has seen several successful implementations that used GPUs to perform raytracing. With the introduction of CUDA programming model this area of research experiences further growth. As more APIs are becoming available one ca...

As the conventional means of analyzing the similarity between a query sequence and database sequences, the Smith-Waterman algorithm is feasible for a database search owing to its high sensitivity. However, this algorithm is still quite time consuming. CUDA programming can improve computations efficiently by using the computational power of massive computing hardware as graphics processing units...

Compound comparison is an important task for the computational chemistry. By the comparison results, potential inhibitors can be found and then used for the pharmacy experiments. The time complexity of a pairwise compound comparison is O(n (2)), where n is the maximal length of compounds. In general, the length of compounds is tens to hundreds, and the computation time is small. However, more a...

With GPU architectures becoming increasingly important due to their large number of parallel processors, NVIDIA’s CUDA environment is becoming widely used to support general purpose applications. To efficiently use the parallel processing power, programmers need to efficiently parallelize and map their algorithms. The difficulty of this task leads to the idea to investigate CUDA’s compiler. Par...

CUDA has been used in many different application domains, not all of which are specifically image processingrelated. There is the opportunity to use multiple and/or distributed CUDA resources in cloud facilities such as Amazon Web Services (AWS), in order to obtain enhanced processing power and to satisfy time-critical requirements which cannot be satisfied using a single CUDA resource. In part...

In recent years, Graphics Processing Units (GPUs) have emerged as a powerful accelerator for general-purpose computations. GPUs are attached to every modern desktop and laptop host CPU as graphics accelerators. GPUs have over a hundred cores with lots of parallelism. Initially, they were used only for graphics applications such as image processing and video games. However, many other applicatio...

GPU-accelerated computing gains rapidly-growing popularity in many areas such as scientific computing, database systems, and cloud environments. However, there are less investigations on the security implications of concurrently running GPU applications. In this paper, we explore security vulnerabilities of CUDA from multiple dimensions. In particular, we first present a study on GPU stack, and...

Parallel computing is a form of computation in which many calculations are carried out simultaneously, operating on the principle that large problems can often be divided into smaller ones, which are then solved concurrently .Now GPU(Graphics Processor Unit) has taken a major role in high performance computing for general purpose applications. Compute Unified Device Architecture (CUDA) programm...