In this paper, we propose a NN search index method called Parallel Quantized Hashing (PQH) for relevant retrieval in Content-Based Image Retrieval (CBIR) systems. Aiming at improving the performance of query, the PQH ...
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ISBN:
(纸本)9780769546766
In this paper, we propose a NN search index method called Parallel Quantized Hashing (PQH) for relevant retrieval in Content-Based Image Retrieval (CBIR) systems. Aiming at improving the performance of query, the PQH partitions the original dataset into several subsets and keeps the pointers to quantized vectors of each subset in a hashing like structure. It only needs to filter parts of the query space to get candidates via neighboring masks and accesses the corresponding real vectors for precise calculation. The neighboring masks are sets of mask vectors indicating cells close to the query point using different metrics. The PQH is multithreaded designed so that the construction and the query can be run in parallel upon multi-core processors. Our extensive experiments confirm that the sequential PQH is of high query efficiency and outperforms the existing methods such as VA+-file and LSH. The parallel run PQH achieves even higher performance and returns appropriate NN results. And more important it is simple for practical implement in real applications.
We introduce an enhanced version of FaST-LMM that maintains the sensitivity of this software when applied to identify epistasis interactions while delivering an acceleration factor that is close to 7.5x on a server eq...
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ISBN:
(纸本)9783319654829;9783319654812
We introduce an enhanced version of FaST-LMM that maintains the sensitivity of this software when applied to identify epistasis interactions while delivering an acceleration factor that is close to 7.5x on a server equipped with a state-of-the-art graphics coprocessor. This performance boost is obtained from the combined effects of integrating a dictionary for faster storage of the test results;a re-organization of the original FaST-LMM Python code;and off-loading of compute-intensive parts to the graphics accelerator.
Heterogeneous computing platforms with multicore host system and many-core accelerator devices have taken a major step forward in the mainstream HPC computing market this year with the announcement of HP Apollo 6000 S...
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ISBN:
(纸本)9781450343510
Heterogeneous computing platforms with multicore host system and many-core accelerator devices have taken a major step forward in the mainstream HPC computing market this year with the announcement of HP Apollo 6000 System's ProLiant XL250a server features the Intel (R) Xeon Phi (TM) coprocessors. Although many application developers attempt to use it in the same way as GPGPU acceleration platforms, doing so forfeits the processing capability of multicore host processors and introduces power inefficiency in business operations. In this paper, we propose an application optimization framework to turn sequential legacy applications into highly parallel applications that make use of the hardware resources both on the host CPU and on the accelerator devices to enable simultaneous heterogeneous computing. As a case study, we look at how to apply this framework and adopt a structured methodology to develop option pricing applications to take advantages of a heterogeneous computing environment.
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