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Modern high performance computing environments are composed of networks of compute nodes that often contain a variety of heterogeneous compute resources, such as multicore CPUs and GPUs. One challenge faced by domain scientists ishow to efficiently use all these distributed, heterogeneous resources. Inorder to use the GPUs effectively, the workload parallelism needs to be muchgreater than the parallelism...
As the scale of modern computing systems grows, failures will happen more frequently. On the way to Exactable a generic, low-overhead, resilient extension becomes a desired aptitude of any programming paradigm. In this paper we explore three additions to a dynamic task-based runtime to build a generic framework providing soft error resilience to task-based programming paradigms. The first recovers...
Ever since accelerators and coprocessors became the mainstream hardware for throughput-oriented HPC workloads, various programming techniques have been proposed to increase productivity in terms of both the performance and ease-of-use. We evaluate these aspects of OpenCL on a number of hardware platforms for an important subset of dense linear algebra operations that are relevant to a wide range of...
Many of the heterogeneous resources available to modern computers are designed for different workloads. In order to efficiently use GPU resources, the workload must have a greater degree of parallelism than a workload designed for multicore-CPUs. And conceptually, the Intel Xeon Phi coprocessors are capable of handling workloads somewhere in between the two. This multitude of applicable workloads...
Recommendation systems are important big data applications that are used in many business sectors of the global economy. While many users utilize Hadoop-like MapReduce systems to implement recommendation systems, we utilize the high-performance shared-memory MapReduce system Phoenix++ [1] to design a faster recommendation engine. In this paper, we design a distributed out-of-core recommendation algorithm...
Recommendation systems are important big data applications that are used in many business sectors of the global economy. While many users utilize Hadoop-like MapReduce systems to implement recommendation systems, we utilize the high-performance shared-memory MapReduce system Phoenix++ [1] to design a faster recommendation engine. In this paper, we design a distributed out-of-core recommendation algorithm...
In this paper, an effective and efficient content-based video copy detection method is proposed. This method is based on temporal features of key frames. Firstly, each video is divided into shots, and each shot is represented by its key frame. Secondly, each key frame is divided into several sub-blocks, and variations of corresponding sub-blocks along key frame series are extracted as video fingerprint...
In this paper, a new method for content-based video copy detection is presented. This method includes fingerprint extracting and matching phases. In the fingerprint extracting phase, a video is represented by a set of Speeded Up Robust Features (SURF), which outperforms other local features. In the fingerprint matching phase, the Locality Sensitive Hashing (LSH) is applied to efficiently detect video...
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