Computing Semantic Similarity Measure Between Words Using Web Search Engine

N, Pushpa C; Girish, S.; Nitin, S K; J, Thriveni; Venugopal, K R; Patnaik, L. M.

doi:10.5121/csit.2013.3514

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…Computing similarities between words has applications such as clustering of documents, detection of relations, disambiguation of entities, finding meta data etc. [29]. Comparison of text documents and web pages can be used for detecting plagiarism.…”

Section: Related Work and Motivationmentioning

confidence: 99%

Benchmarking Performance of a Hybrid Intel Xeon/Xeon Phi System for Parallel Computation of Similarity Measures Between Large Vectors

Czarnul

2016

Int J Parallel Prog

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The paper deals with parallelization of computing similarity measures between large vectors. Such computations are important components within many applications and consequently are of high importance. Rather than focusing on optimization of the algorithm itself, assuming specific measures, the paper assumes a general scheme for finding similarity measures for all pairs of vectors and investigates optimizations for scalability in a hybrid Intel Xeon/Xeon Phi system. Hybrid systems including multicore CPUs and many-core compute devices such as Intel Xeon Phi allow parallelization of such computations using vectorization but require proper load balancing and optimization techniques. The proposed implementation uses C/OpenMP with the offload mode to Xeon Phi cards. Several results are presented: execution times for various partitioning parameters such as batch sizes of vectors being compared, impact of dynamic adjustment of batch size, overlapping computations and communication. Execution times for comparison of all pairs of vectors are presented as well as those for which similarity measures account for a predefined threshold. The latter makes load balancing more difficult and is used as a benchmark for the proposed optimizations. Results are presented for the native mode on an Intel Xeon Phi, CPU only and the CPU + offload mode for a hybrid system with 2 Intel Xeons with 20 physical cores and 40 logical processors and 2 Intel Xeon Phis with a total of 120 physical cores and 480 logical processors.

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Section: Related Work and Motivationmentioning

confidence: 99%

Benchmarking Performance of a Hybrid Intel Xeon/Xeon Phi System for Parallel Computation of Similarity Measures Between Large Vectors

Czarnul

2016

Int J Parallel Prog

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“…Applications of similarity computations are very broad and are often based on comparison of components such as images, words, text excerpts, documents (text, web pages) and various objects [22,24]. These components can be encoded as multidimensional vectors.…”

Section: Related Workmentioning

confidence: 99%

“…There are numerous applications of similarity computations including: providing hints to the user during web searching [22], finding data related to the current clinical case in medicine [17], disambiguation of entities [18], document clusterization [25], audio recognition [19], handwritten word image retrieval [23], linguistic information classification [21]. Paper [16] discusses benefits from parallelization of chemical similarity calculation and presents an algorithm for all-vs-all Tanimoto matrix calculation and nearest neighbor search using GPUs demonstrating considerable benefits over CPU-based approaches.…”

Section: Related Workmentioning

confidence: 99%

Parallelization of large vector similarity computations in a hybrid CPU+GPU environment

Czarnul

2017

J Supercomput

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The paper presents design, implementation and tuning of a hybrid parallel OpenMP+CUDA code for computation of similarity between pairs of a large number of multidimensional vectors. The problem has a wide range of applications, and consequently its optimization is of high importance, especially on currently widespread hybrid CPU+GPU systems targeted in the paper. The following are presented and tested for computation of all vector pairs: tuning of a GPU kernel with consideration of memory coalescing and using shared memory, minimization of GPU memory allocation costs, optimization of CPU-GPU communication in terms of size of data sent, overlapping CPU-GPU communication and kernel execution, concurrent kernel execution, determination of best sizes for data batches processed on CPUs and GPUs along with best GPU grid sizes. It is shown that all codes scale in hybrid environments with various relative performances of compute devices, even for a case when comparisons of various vector pairs take various amounts of time. Tests were performed on two high-performance hybrid systems with: 2 x Intel Xeon E5-2640 CPU + 2 x NVIDIA Tesla K20m and latest generation 2 x Intel Xeon CPU E5-2620 v4 + NVIDIA's Pascal generation GTX 1070 cards. Results demonstrate expected improvements and beneficial optimizations important for users incorporating such types of computations into their parallel codes run on similar systems.

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Computing Semantic Similarity Measure Between Words Using Web Search Engine

Abstract: Semantic Similarity measures between words plays an important role in information retrieval

Cited by 2 publications

References 6 publications

Benchmarking Performance of a Hybrid Intel Xeon/Xeon Phi System for Parallel Computation of Similarity Measures Between Large Vectors

Benchmarking Performance of a Hybrid Intel Xeon/Xeon Phi System for Parallel Computation of Similarity Measures Between Large Vectors

Parallelization of large vector similarity computations in a hybrid CPU+GPU environment

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