The Experts below are selected from a list of 4812 Experts worldwide ranked by ideXlab platform
Kurt Keutzer - One of the best experts on this subject based on the ideXlab platform.
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parallel bfs Graph Traversal on images using structured grid
International Conference on Image Processing, 2010Co-Authors: Tasneem Brutch, Kurt KeutzerAbstract:Graph algorithms are widely used in image processing techniques. With technology advancements, image sizes are increasing, and the contents inside images are becoming more complex, resulting in increased runtimes for Graph algorithms on these images. Breadth First Search (BFS) is a fundamental Graph Traversal approach. A key to parallelizing Graph algorithms used in image processing is to parallelize the BFS Graph Traversal operation. In this paper, we propose using highly parallelizable structured grid computations to realize the BFS Graph Traversal operations. This mapping enables efficient implementation of the BFS Graph Traversal operations on highly parallel manycore platforms. By using such a mapping, we were able to achieve performance gains of 2× to 33× depending on image complexity.
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scalable hmm based inference engine in large vocabulary continuous speech recognition
International Conference on Multimedia and Expo, 2009Co-Authors: Jike Chong, Ekaterina Gonina, Kisun You, Christopher J Hughes, Wonyong Sung, Kurt KeutzerAbstract:Parallel scalability allows an application to efficiently utilize an increasing number of processing elements. In this paper we explore a design space for application scalability for an inference engine in large vocabulary continuous speech recognition (LVCSR). Our implementation of the inference engine involves a parallel Graph Traversal through an irregular Graph-based knowledge network with millions of states and arcs. The challenge is not only to define a software architecture that exposes sufficient fine-grained application concurrency, but also to efficiently synchronize between an increasing number of concurrent tasks and to effectively utilize the parallelism opportunities in today's highly parallel processors. We propose four application-level implementation alternatives we call “algorithm styles”, and construct highly optimized implementations on two parallel platforms: an Intel Core i7 multicore processor and a NVIDIA GTX280 manycore processor. The highest performing algorithm style varies with the implementation platform. On 44 minutes of speech data set, we demonstrate substantial speedups of 3.4× on Core i7 and 10.5× on GTX280 compared to a highly optimized sequential implementation on Core i7 without sacrificing accuracy. The parallel implementations contain less than 2.5% sequential overhead, promising scalability and significant potential for further speedup on future platforms.
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a fully data parallel wfst based large vocabulary continuous speech recognition on a Graphics processing unit
Conference of the International Speech Communication Association, 2009Co-Authors: Jike Chong, Ekaterina Gonina, Youngmin Yi, Kurt KeutzerAbstract:Tremendous compute throughput is becoming available in personal desktop and laptop systems through the use of Graphics processing units (GPUs). However, exploiting this resource requires re-architecting an application to fit a data parallel programming model. The complex Graph Traversal routines in the inference process for large vocabulary continuous speech recognition (LVCSR) have been considered by many as unsuitable for extensive parallelization. We explore and demonstrate a fully data parallel implementation of a speech inference engine on NVIDIA’s GTX280 GPU. Our implementation consists of two phases compute-intensive observation probability computation phase and communication-intensive Graph Traversal phase. We take advantage of dynamic elimination of redundant computation in the compute-intensive phase while maintaining close-to-peak execution efficiency. We also demonstrate the importance of exploring application-level trade-offs in the communication-intensive Graph Traversal phase to adapt the algorithm to data parallel execution on GPUs. On 3.1 hours of speech data set, we achieve more than 11× speedup compared to a highly optimized sequential implementation on Intel Core i7 without sacrificing accuracy.
Wellein Gerhard - One of the best experts on this subject based on the ideXlab platform.
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A Recursive Algebraic Coloring Technique for Hardware-Efficient Symmetric Sparse Matrix-Vector Multiplication
'Association for Computing Machinery (ACM)', 2020Co-Authors: Alappat, Christie Louis, Hager Georg, Schenk Olaf, Thies Jonas, Basermann Achim, Bishop, Alan R., Fehske Holger, Wellein GerhardAbstract:The symmetric sparse matrix-vector multiplication (SymmSpMV) is an important building block for many numerical linear algebra kernel operations or Graph Traversal applications. Parallelizing SymmSpMV on today's multicore platforms with up to 100 cores is difficult due to the need to manage conflicting updates on the result vector. Coloring approaches can be used to solve this problem without data duplication, but existing coloring algorithms do not take load balancing and deep memory hierarchies into account, hampering scalability and full-chip performance. In this work, we propose the recursive algebraic coloring engine (RACE), a novel coloring algorithm and open-source library implementation, which eliminates the shortcomings of previous coloring methods in terms of hardware efficiency and parallelization overhead. We describe the level construction, distance-k coloring, and load balancing steps in RACE, use it to parallelize SymmSpMV, and compare its performance on 31 sparse matrices with other state-of-the-art coloring techniques and Intel MKL on two modern multicore processors. RACE outperforms all other approaches substantially and behaves in accordance with the Roofline model. Outliers are discussed and analyzed in detail. While we focus on SymmSpMV in this paper, our algorithm and software is applicable to any sparse matrix operation with data dependencies that can be resolved by distance-k coloring
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A Recursive Algebraic Coloring Technique for Hardware-Efficient Symmetric Sparse Matrix-Vector Multiplication
'Association for Computing Machinery (ACM)', 2019Co-Authors: Alappat, Christie L., Hager Georg, Schenk Olaf, Thies Jonas, Basermann Achim, Bishop, Alan R., Fehske Holger, Wellein GerhardAbstract:The symmetric sparse matrix-vector multiplication (SymmSpMV) is an important building block for many numerical linear algebra kernel operations or Graph Traversal applications. Parallelizing SymmSpMV on today's multicore platforms with up to 100 cores is difficult due to the need to manage conflicting updates on the result vector. Coloring approaches can be used to solve this problem without data duplication, but existing coloring algorithms do not take load balancing and deep memory hierarchies into account, hampering scalability and full-chip performance. In this work, we propose the recursive algebraic coloring engine (RACE), a novel coloring algorithm and open-source library implementation, which eliminates the shortcomings of previous coloring methods in terms of hardware efficiency and parallelization overhead. We describe the level construction, distance-k coloring, and load balancing steps in RACE, use it to parallelize SymmSpMV, and compare its performance on 31 sparse matrices with other state-of-the-art coloring techniques and Intel MKL on two modern multicore processors. RACE outperforms all other approaches substantially and behaves in accordance with the Roofline model. Outliers are discussed and analyzed in detail. While we focus on SymmSpMV in this paper, our algorithm and software is applicable to any sparse matrix operation with data dependencies that can be resolved by distance-k coloring.Comment: 40 pages, 23 figure
Niranjan Nagarajan - One of the best experts on this subject based on the ideXlab platform.
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fast and sensitive mapping of nanopore sequencing reads with Graphmap
Nature Communications, 2016Co-Authors: Ivan Sovic, Shannon N Fenlon, Andreas Wilm, Mile Sikic, Swaine L. Chen, Niranjan NagarajanAbstract:Realizing the democratic promise of nanopore sequencing requires the development of new bioinformatics approaches to deal with its specific error characteristics. Here we present GraphMap, a mapping algorithm designed to analyse nanopore sequencing reads, which progressively refines candidate alignments to robustly handle potentially high-error rates and a fast Graph Traversal to align long reads with speed and high precision (>95%). Evaluation on MinION sequencing data sets against short- and long-read mappers indicates that GraphMap increases mapping sensitivity by 10–80% and maps >95% of bases. GraphMap alignments enabled single-nucleotide variant calling on the human genome with increased sensitivity (15%) over the next best mapper, precise detection of structural variants from length 100 bp to 4 kbp, and species and strain-specific identification of pathogens using MinION reads. GraphMap is available open source under the MIT license at https://github.com/isovic/Graphmap.
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fast and sensitive mapping of error prone nanopore sequencing reads with Graphmap
bioRxiv, 2015Co-Authors: Ivan Sovic, Shannon N Fenlon, Andreas Wilm, Mile Sikic, Swaine L. Chen, Niranjan NagarajanAbstract:Exploiting the power of nanopore sequencing requires the development of new bioinformatics approaches to deal with its specific error characteristics. We present the first nanopore read mapper (GraphMap) that uses a read-funneling paradigm to robustly handle variable error rates and fast Graph Traversal to align long reads with speed and very high precision (>95%). Evaluation on MinION sequencing datasets against short and long-read mappers indicates that GraphMap increases mapping sensitivity by at least 15-80%. GraphMap alignments are the first to demonstrate consensus calling with <1 error in 100,000 bases, variant calling on the human genome with 76% improvement in sensitivity over the next best mapper (BWA-MEM), precise detection of structural variants from 100bp to 4kbp in length and species and strain-specific identification of pathogens using MinION reads. GraphMap is available open source under the MIT license at https://github.com/isovic/Graphmap.
Emanuel Sallinger - One of the best experts on this subject based on the ideXlab platform.
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vadalog a modern architecture for automated reasoning with large knowledge Graphs
Information Systems, 2020Co-Authors: Luigi Bellomarini, Davide Benedetto, Georg Gottlob, Emanuel SallingerAbstract:Abstract The introduction of novel Datalog +/- fragments with good theoretical properties, together with the growing use of enterprise knowledge Graphs motivated the development of Vadalog, a knowledge Graph management system developed at the University of Oxford. It adopts Warded Datalog +/- as the core of its language for knowledge representation and reasoning, which exhibits a very good tradeoff between computational complexity of reasoning and expressive power, capturing PTIME data complexity while allowing ontological reasoning and full recursion. In this paper, we provide a detailed illustration of the Vadalog system, presenting: the essentials of the first implementation of Warded Datalog +/-; a comprehensive overview of the architecture with specific focus on runtime execution model, memory management, Graph Traversal strategies and join algorithms; and a detailed experimental evaluation. This paper is a substantially expanded version of the AMW 2019 paper “Datalog-based reasoning for Knowledge Graphs”. To stand apart from previous works on the topic, our focus in this work shall be a comprehensive presentation of the architecture of the Vadalog system and showing how our techniques work together to provide a full-fledged KGMS. In particular, roughly half of this paper is new material created particularly for this comprehensive architectural view. This includes a new series of experiments designed to shed light on architectural choices and alternatives.
Shannon N Fenlon - One of the best experts on this subject based on the ideXlab platform.
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fast and sensitive mapping of nanopore sequencing reads with Graphmap
Nature Communications, 2016Co-Authors: Ivan Sovic, Shannon N Fenlon, Andreas Wilm, Mile Sikic, Swaine L. Chen, Niranjan NagarajanAbstract:Realizing the democratic promise of nanopore sequencing requires the development of new bioinformatics approaches to deal with its specific error characteristics. Here we present GraphMap, a mapping algorithm designed to analyse nanopore sequencing reads, which progressively refines candidate alignments to robustly handle potentially high-error rates and a fast Graph Traversal to align long reads with speed and high precision (>95%). Evaluation on MinION sequencing data sets against short- and long-read mappers indicates that GraphMap increases mapping sensitivity by 10–80% and maps >95% of bases. GraphMap alignments enabled single-nucleotide variant calling on the human genome with increased sensitivity (15%) over the next best mapper, precise detection of structural variants from length 100 bp to 4 kbp, and species and strain-specific identification of pathogens using MinION reads. GraphMap is available open source under the MIT license at https://github.com/isovic/Graphmap.
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fast and sensitive mapping of error prone nanopore sequencing reads with Graphmap
bioRxiv, 2015Co-Authors: Ivan Sovic, Shannon N Fenlon, Andreas Wilm, Mile Sikic, Swaine L. Chen, Niranjan NagarajanAbstract:Exploiting the power of nanopore sequencing requires the development of new bioinformatics approaches to deal with its specific error characteristics. We present the first nanopore read mapper (GraphMap) that uses a read-funneling paradigm to robustly handle variable error rates and fast Graph Traversal to align long reads with speed and very high precision (>95%). Evaluation on MinION sequencing datasets against short and long-read mappers indicates that GraphMap increases mapping sensitivity by at least 15-80%. GraphMap alignments are the first to demonstrate consensus calling with <1 error in 100,000 bases, variant calling on the human genome with 76% improvement in sensitivity over the next best mapper (BWA-MEM), precise detection of structural variants from 100bp to 4kbp in length and species and strain-specific identification of pathogens using MinION reads. GraphMap is available open source under the MIT license at https://github.com/isovic/Graphmap.
