Jianting Zhang, Simin You, Le Gruenwald
City-wide GPS recorded taxi trip data contains rich information for traffic and travel analysis to facilitate transportation planning and urban studies. However, traditional data management techniques are largely incapable of processing big taxi trip data at the scale of hundreds of millions. In this study, we aim at utilizing the General Purpose computing on Graphics […]
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Ekaterina I. Gonina
Developing efficient parallel implementations and fully utilizing the available resources of parallel platforms is now required for software applications to scale to new generations of processors. Yet, parallel programming remains challenging to programmers due to the requisite low-level knowledge of the underlying hardware and parallel computing constructs. These restrictions in turn impede experimentation with various […]
Benjamin Y. Cho, Won Seob Jeong, Doohwan Oh, Won Woo Ro
Considerable research has been conducted recently on near-data processing techniques as real-world tasks increasingly involve large-scale and high-dimensional data sets. The advent of solid-state drives (SSDs) has spurred further research because of their processing capability and high internal bandwidth. However, the data processing capability of conventional SSD systems have not been impressive. In particular, they […]
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Feng Ji
GPU has become a popular parallel accelerator in modern heterogeneous systems for its great parallelism and superior energy efficiency. However, it also extremely complicates programing the memory system in such heterogeneous systems, due to the non-continuous memory spaces on CPU and GPU, and a two-level memory hierarchy on a GPU itself. The complexity of this […]
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Mengjun Xie, Kyoung-Don Kang, Can Basaran
MapReduce greatly decrease the complexity of developing applications for parallel data processing. To considerably improve the performance of MapReduce applications, we design a new MapReduce framework, called Moim, which 1) effectively utilizes both CPUs and GPUs (general purpose Graphics Processing Units), 2) overlaps CPU and GPU computations, 3) enhances load balancing in the map and […]
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Mian Lu, Lei Zhang, Huynh Phung Huynh, Zhongliang Ong, Yun Liang, Bingsheng He, Rick Siow Mong Goh, Richard Huynh
With the ease-of-programming, flexibility and yet efficiency, MapReduce has become one of the most popular frameworks for building big-data applications. MapReduce was originally designed for distributed-computing, and has been extended to various architectures, e,g, multi-core CPUs, GPUs and FPGAs. In this work, we focus on optimizing the MapReduce framework on Xeon Phi, which is the […]
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Peter Wittek, Sandor Daranyi
Scientific computations have been using GPU-enabled computers successfully, often relying on distributed nodes to overcome the limitations of device memory. Only a handful of text mining applications benefit from such infrastructure. Since the initial steps of text mining are typically data intensive, and the ease of deployment of algorithms is an important factor in developing […]
Lesley Northam, Khuzaima Daudjee, Rob Smits, Joe Istead
We present the Hadoop Online Ray Tracer (HORT), a scalable ray tracing framework for general, pay-as-you-go, cloud computing services. Using MapReduce, HORT partitions the computational workload and scene data differently than other distributed memory ray tracing frameworks. We show that this unique partitioning significantly bounds the data replication costs and inter-process communication. Consequently HORT is […]
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Max Grossman, Mauricio Breternitz, Vivek Sarkar
As the scale of high performance computing systems grows, three main challenges arise: the programmability, reliability, and energy efficiency of those systems. Accomplishing all three without sacrificing performance requires a rethinking of legacy distributed programming models and homogeneous clusters. In this work, we integrate Hadoop MapReduce with OpenCL to enable the use of heterogeneous processors […]
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Marwa Khamis Elteir
Nowadays, an increasing number of computational systems are equipped with heterogeneous compute resources, i.e., following different architecture. This applies to the level of a single chip, a single node and even supercomputers and large-scale clusters. With its impressive price-to-performance ratio as well as power efficiency compared to traditional multicore processors, graphics processing units (GPUs) has […]
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Ettore Speziale
Today, parallel architectures are the main vector for exploiting available die area. The shift from architectures tuned for sequential programming models to ones optimized for parallel processing follows from the inability of further enhance sequential performance due to power and memory walls. On the other hand, efficient exploitation of parallel computing units looks a hard […]
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Jon Currey, Simon Baker, Christopher J. Rossbach
Dataflow execution engines such as MapReduce, DryadLINQ, and PTask have enjoyed success because they simplify development for a class of important parallel applications. These systems sacrifice generality for simplicity: while many workloads are easily expressed, important idioms like iteration and recursion are difficult to express and support efficiently. We consider the problem of extending a […]
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