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Dayong Wang, Anil K. Jain
Face retrieval is an enabling technology for many applications, including automatic face annotation, deduplication, and surveillance. In this paper, we propose a face retrieval system which combines a k-NN search procedure with a COTS matcher (PittPatt) in a cascaded manner. In particular, given a query face, we first pre-filter the gallery set and find the […]
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Johannes Koster
The analysis of next-generation sequencing (NGS) data is a major topic in bioinformatics: short reads obtained from DNA, the molecule encoding the genome of living organisms, are processed to provide insight into biological or medical questions. This thesis provides novel solutions to major topics within the analysis of NGS data, focusing on parallelization, scalability and […]
Vasvi Kakkad
Advances in technology have given rise to applications that are deployed on wireless sensor networks (WSNs), the cloud, and the Internet of things. There are many emerging applications, some of which include sensor-based monitoring, web traffic processing, and network monitoring. These applications collect large amount of data as an unbounded sequence of events and process […]
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Krzysztof Kaczmarski, Pawel Rzazewski, Albert Wolant
Motion planning is an important and well-studied field of robotics. A typical approach to finding a route is to construct a cell graph representing a scene and then to find a path in such a graph. In this paper we present and analyze parallel algorithms for constructing the cell graph on a SIMD-like GPU processor. […]
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Kyuyeon Hwang, Wonyong Sung
Recurrent neural networks (RNNs) have shown outstanding performance on processing sequence data. However, they suffer from long training time, which demands parallel implementations of the training procedure. Parallelization of the training algorithms for RNNs are very challenging because internal recurrent paths form dependencies between two different time frames. In this paper, we first propose a […]
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Weifeng Liu, Brian Vinter
Sparse matrix-vector multiplication (SpMV) is a fundamental building block for numerous applications. In this paper, we propose CSR5 (Compressed Sparse Row 5), a new storage format, which offers high-throughput SpMV on various platforms including CPUs, GPUs and Xeon Phi. First, the CSR5 format is insensitive to the sparsity structure of the input matrix. Thus the […]
Yunjin Chen, Wei Yu, Thomas Pock
For several decades, image restoration remains an active research topic in low-level computer vision and hence new approaches are constantly emerging. However, many recently proposed algorithms achieve state-of-the-art performance only at the expense of very high computation time, which clearly limits their practical relevance. In this work, we propose a simple but effective approach with […]
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Ken Miura, Tatsuya Harada
Deep learning can achieve outstanding results in various fields. However, it requires so significant computational power that graphics processing units (GPUs) and/or numerous computers are often required for the practical application. We have developed a new distributed calculation framework called "Sashimi" that allows any computer to be used as a distribution node only by accessing […]
Lukas Polok, Viorela Ila, Pavel Smrz
Sparse matrix multiplication is an important algorithm in a wide variety of problems, including graph algorithms, simulations and linear solving to name a few. Yet, there are but a few works related to acceleration of sparse matrix multiplication on a GPU. We present a fast, novel algorithm for sparse matrix multiplication, outperforming the previous algorithm […]
Scott Sallinen, Abdullah Gharaibeh, Matei Ripeanu
Large scale-free graphs are famously difficult to process efficiently: the highly skewed vertex degree distribution makes it difficult to obtain balanced workload partitions for parallel processing. Our research instead aims to take advantage of vertex degree heterogeneity by partitioning the workload to match the strength of the individual computing elements in a hybrid architecture. This […]
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Kooktae Lee, Raktim Bhattacharya, Vijay Gupta
In the near future, massively parallel computing systems will be necessary to solve computation intensive applications. The key bottleneck in massively parallel implementation of numerical algorithms is the synchronization of data across processing elements (PEs) after each iteration, which results in significant idle time. Thus, there is a trend towards relaxing the synchronization and adopting […]
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Lukas Polok, Viorela Ila, Pavel Smrz
Fast sorting is an important step in many parallel algorithms, which require data ranking, ordering or partitioning. Parallel sorting is a widely researched subject, and many algorithms were developed in the past. In this paper, the focus is on implementing highly efficient sorting routines for the sparse linear algebra operations, such as parallel sparse matrix […]
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