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We propose a design for a fine-grained lock-based skiplist optimized for Graphics Processing Units (GPUs). While GPUs are often used to accelerate streaming parallel computations, it remains a significant challenge to efficiently offload concurrent computations with more complicated data-irregular access and fine-grained synchronization. Natural building blocks for such computations would be concurrent...
The increasing programability and the high computational power of Graphical Processing Units (GPU) make them attractive to general purpose programming. However, taking full benefit of this execution environment is a challenging task. One of these challenges stem from divergences, a phenomenon that occurs when threads that execute in lock-step are forced to take different program paths due to branches...
The Graphics Processing Unit (GPU) is an asymmetric, heterogeneous multi-core architecture that can be used for high performance parallel computing applications. However, a significant level of interest has been focused on algorithms for solving regular problems, as these applications typically map well to the GPU. Irregular applications, which rely on pointer or graph-based data structures, have...
We present a new approach for parallel massive graph analysis of streaming, temporal data with a dynamic and extensible representation. Handling the constant stream of new data from health care, security, business, and social network applications requires new algorithms and data structures. We examine data structure and algorithm trade-offs that extract the parallelism necessary for high-performance...
Modern GPUs open a completely new field to optimize embarrassingly parallel algorithms. Implementing an algorithm on a GPU confronts the programmer with a new set of challenges for program optimization. Some of the most notable ones are isolating the part of the algorithm that can be optimized to run on the GPU; tuning the program for the GPU memory hierarchy whose organization and performance implications...
Graph-theoretic abstractions are extensively used to analyze massive data sets. Temporal data streams from socio-economic interactions, social networking Web sites, communication traffic, and scientific computing can be intuitively modeled as graphs. We present the first study of novel high-performance combinatorial techniques for analyzing largescale information networks, encapsulating dynamic interaction...
In a real-time Linux system, the critical sections are known as the main factor delaying the execution of real-time tasks. Traditional approaches to overcoming this issue have given less consideration to both real-time and non-real-time tasks. In this paper, we propose a new lock management mechanism to improve the real-time performance with a small penalty for non-real-time tasks. Using this mechanism,...
Characteristics of full applications found in scientific computing industries today lead to challenges that are not addressed by state-of-the-art approaches to automatic parallelization.These characteristics are not present in CPU kernel codes nor linear algebra libraries, requiring a fresh look at how to make automatic parallelization apply to today's computational industries using full applications...
Information infection and information leakage in computer systems are mainly caused by insecure network access. Considering the particularity of network security, a tool DTAD (dynamic taint analysis detector) for information flow security detection is designed and implemented, aiming at the problem of data security in network access. This tool performs log recording and state controlling for malicious...
Modern microprocessors are becoming increasingly parallel devices, and GPUs are at the leading edge of this trend. Designing parallel algorithms for manycore chips like the GPU can present interesting challenges, particularly for computations on sparse data structures. One particularly common example is the collection of sparse matrix solvers and combinatorial graph algorithms that form the core of...
We analyze and compare several data structures and algorithms for evaluating the kernel density estimate. Frequent evaluations of this estimate are for example needed for plotting, error estimation, Monte Carlo estimation of probabilities and functionals, and pattern classification. An experimental comparison is included.
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