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2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016

作者： Ji, Shihao Yun, Hyokun Yanardag, Pinar Matsushima, Shin Vishwanathan, S.V.N. Parallel Computing Lab Intel Amazon Purdue University United States University of Tokyo Japan Univ. of California Santa Cruz United States

ISBN: (纸本)9781945626258

Embedding words in a vector space has gained a lot of attention in recent years. While state-of-the-art methods provide efficient computation of word similarities via a low-dimensional matrix embedding, their motivation is often left unclear. In this paper, we argue that word embedding can be naturally viewed as a ranking problem due to the ranking nature of the evaluation metrics. Then, based on this insight, we propose a novel framework WordRank that efficiently estimates word representations via robust ranking, in which the attention mechanism and robustness to noise are readily achieved via the DCG-like ranking losses. The performance of WordRank is measured in word similarity and word analogy benchmarks, and the results are compared to the state-of-the-art word embedding techniques. Our algorithm is very competitive to the state-of-the- arts on large corpora, while outperforms them by a significant margin when the training set is limited (i.e., sparse and noisy). With 17 million tokens, WordRank performs almost as well as existing methods using 7.2 billion tokens on a popular word similarity benchmark. Our multi-node distributed implementation of WordRank is publicly available for general usage. © 2016 Association for Computational Linguistics

关键词： Vector spaces

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Accelerating linpack performance with mixed precision algorithm on CPU+GPGPU heterogeneous cluster

Accelerating linpack performance with mixed precision algori...

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10th IEEE International Conference on Computer and Information Technology, CIT-2010, 7th IEEE International Conference on Embedded Software and Systems, ICESS-2010, 10th IEEE Int. Conf. Scalable computing and Communications, ScalCom-2010

作者： Wang, Lei Zhang, Yunquan Zhang, Xianyi Liu, Fangfang Lab of Parallel Computing Institute of Software Chinese Academy of Sciences Beijing 100190 China State Key Lab of Computing Science Chinese Academy of Sciences Beijing 100190 China Chinese Academy of Sciences Graduate University Beijing 100190 China

ISBN: (纸本)9780769541082

In this paper, the mixed precision algorithm to solve the linear system of equations and the implementation of HPL package are introduced. We use this mixed precision algorithm to improve HPL package on CPU+GPGPU heterogeneous clusters, which is named for GHPL, and give the implementation mechanisms in detail. The experimental results are measured on the platforms of multi-core CPUs and CPU+GPGPU heterogeneous clusters. From the experimental results, we can find out that our GHPL program has good scalability on all the experimental environments and can sustain more than 1.7Teraflops both on the cluster with 16 nodes containing 32 NVIDIA Tesla C1060 GPUs and on the cluster with 8 nodes containing 32 NVIDIA GeForce GTX 295 GPUs, while the average speedup of it with respect to HPL is 3.06 and 2.40 respectively. © 2010 IEEE.

关键词： Program processors

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Accelerating Deep Learning based Identification of Chromatin Accessibility from noisy ATAC-seq Data

Accelerating Deep Learning based Identification of Chromatin...

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IEEE International Symposium on parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Narendra Chaudhary Sanchit Misra Dhiraj Kalamkar Alexander Heinecke Evangelos Georganas Barukh Ziv Menachem Adelman Bharat Kaul Parallel Computing Lab Intel Bangalore India parallel Computing Lab Intel Corporation Santa Clara USA Intel Corporation Haifa Israel

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Identifying accessible chromatin regions is a fundamental problem in epigenomics with ATAC-seq being a commonly used assay. Exponential rise in ATAC-seq experiments has made it critical to accelerate processing of ATAC-seq data that can have a low signal-to-noise ratio for various reasons including low coverage or low cell count. To denoise and identify accessible chromatin regions from noisy ATAC-seq data, use of deep learning on 1D data - using large filter sizes, long tensor widths, and/or dilation - has recently been proposed. Convolutions over 1D data consume a majority of the runtime in these methods. However, existing implementations of the 1D convolution layer for CPUs and GPUs fail to efficiently use the underlying architecture especially in the case of large filter sizes, long tensor widths, and dilation. Here, we present ways to accelerate the end-to-end training performance of these deep learning based methods. We evaluate our approach on the recently released AtacWorks toolkit using modern CPUs. Compared to AtacWorks running on an Nvidia DGX-1 box with 8 V100 GPUs, we get up to 2.27× speedup using just 16 CPU sockets. To achieve this, we build an efficient 1D dilated convolution layer and demonstrate reduced precision (BFloat16) training and nearly linear scaling from 1 to 16 sockets. Code Availability: https://***/Intellabs/Trans-Omics-Acceleration-Library/tree/ATAC-Seq/applications/ATAC-Seq

关键词： Deep learning Convolutional codes Training Tensors Costs Convolution Computer architecture

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Mixed low-precision deep learning inference using dynamic fixed point

arXiv

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arXiv 2017年

作者： Mellempudi, Naveen Kundu, Abhisek Das, Dipankar Mudigere, Dheevatsa Kaul, Bharat Parallel Computing Lab Intel Labs Bangalore India

We propose a cluster-based quantization method to convert pre-trained full precision weights into ternary weights with minimal impact on the accuracy. In addition we also constrain the activations to 8-bits thus enabling sub 8-bit full integer inference pipeline. Our method uses smaller clusters of N filters with a common scaling factor to minimize the quantization loss, while also maximizing the number of ternary operations. We show that with cluster size of N=4 on Resnet-101, can achieve 71.8% TOP-1 accuracy, within 6% of the best full precision result, while replacing ≈ 85% of all multiplications with 8-bit accumulations. Using the same method with 4-bit weights achieves 76.3% TOP-1 accuracy which within 2% of the full precision result. We also study the impact of the size of the cluster on both performance and accuracy, larger cluster sizes N=64 can replace ≈ 98% of the multiplications with ternary operations but introduces significant drop in accuracy which necessitates fine tuning the parameters with retraining the network at lower precision. To address this we have also trained low-precision Resnet-50 with 8-bit activations and ternary weights by pre-initializing the network with full precision weights and achieve 68.9% TOP-1 accuracy within 4 additional epochs. Our final quantized model can run on a full 8-bit compute pipeline, with a potential 16x improvement in performance compared to baseline full-precision models. Copyright © 2017, The Authors. All rights reserved.

关键词： Chemical activation

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A Multi-Dimensional Assessment Model and Its Application in E-learning Courses of Computer Science 20

A Multi-Dimensional Assessment Model and Its Application in ...

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21st Annual Conference of the Special Interest Group in Information Technology Education, SIGITE 2020

作者： Luo, Jiwen Lu, Feng Wang, Tao Key Lab. of Parallel and Distributed Computing College of Computer National University of Defense Technology Changsha China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781450370455

Computer science is a practical discipline. It is always a great challenge to evaluate students' computer practice using computer-aided means for large scale students. We always need to address problems such as suspected plagiarism and deviation of the overall difficulty factor. In this paper, a multi-dimensional assessment model is designed for CS courses based on the detailed practice processing data in an E-learning system. The model comprehensively evaluates the students' learning process and results in three aspects of correctness, originality, and quality detection. Besides, the teacher can easily participate in the assessment according to their needs. The correctness is an essential requirement, and the originality is based on the clustering results of students' behaviors after clone detection to curb homework plagiarism. SonarQube is used to detect code quality and put forward higher requirements for codes. Manual participation intelligence has improved the flexibility and applicability of the model to a certain extent. We applied this model on the EduCoder online education platform and carried out a comprehensive analysis of 485 students in the parallel Programming Principles and Practice Class of Huazhong University of Science and Technology. Experiment results confirm the distinction, rationality, and fairness of the model in assessing student performance. It not only gives students a credible, comprehensive score in large-scale online practical programming courses but also gives teachers and students corresponding suggestions based on the evaluation results. Furthermore, the model can be extended to other online education platforms. © 2020 ACM.

关键词： Students

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Toward scalable verification for safety-critical deep networks

arXiv

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arXiv 2018年

作者： Kuper, Lindsey Katz, Guy Gottschlich, Justin Julian, Kyle Barrett, Clark Kochenderfer, Mykel J. Parallel Computing Lab Intel Labs Stanford University

The increasing use of deep neural networks for safety-critical applications, such as autonomous driving and flight control, raises concerns about their safety and reliability. Formal verification can address these concerns by guaranteeing that a deep learning system operates as intended, but the state of the art is limited to small systems. In this work-in-progress report we give an overview of our work on mitigating this difficulty, by pursuing two complementary directions: devising scalable verification techniques, and identifying design choices that result in deep learning systems that are more amenable to verification. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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To INT-MAX... and Beyond! Exploring Large-Count Support in MPI

To INT-MAX... and Beyond! Exploring Large-Count Support in M...

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2014 Workshop on Exascale MPI at Supercomputing Conference, ExaMPI 2014

作者： Hammond, Jeff R. Schafer, Andreas Latham, Rob Parallel Computing Lab Intel Corp. Switzerland Friedrich-Alexander-Universitat Erlangen-Nurnberg Germany Mathmatics and Computer Science Division Argonne National Lab. United States

ISBN: (纸本)9781479970322

In order to describe a structured region of memory, the routines in the MPI standard use a (count, datatype) pair. The C specification for this convention uses an int type for the count. Since C int types are nearly always 32 bits large and signed, counting more than 231 elements poses a challenge. Instead of changing the existing MPI routines, and all consumers of those routines, the MPI Forum asserts that users can build up large datatypes from smaller types. To evaluate this hypothesis and to provide a user-friendly solution to the large-count issue, we have developed BigMPI, a library on top of MPI that maps large-count MPI-like functions to MPI-3 standard features. BigMPI demonstrates a way to perform such a construction, reveals shortcomings of the MPI standard, and uncovers bugs in MPI implementations. © 2014 IEEE.

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Analysis of Insiders Attack Mitigation Strategies

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Procedia - Social and Behavioral Sciences 2014年 129卷 611-618页

作者： Zulkefli Mohd Yusop Jemal H. Abawajy Parallel and Distributed Computing Lab School of Information Technology Deakin University Victoria Australia

Insider threat has become a serious information security issues within organizations. In this paper, we analyze the problem of insider threats with emphases on the Cloud computing platform. Security is one of the major anxieties when planning to adopt the Cloud. This paper will contribute towards the conception of mitigation strategies that can be relied on to solve the malicious insider threats. While Cloud computing relieves organizations from the burden of the data management and storage costs, security in general and the malicious insider threats in particular is the main concern in cloud environments. We will analyses the existing mitigation strategies to reduce malicious insiders threats in Cloud computing.

关键词： Cloud computing Insider Attacks Malicious Insider Collusions Mitigation Strategies Cloud computing Security Data Security.

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Data-race detection: The missing piece for an end-to-end semantic equivalence checker for parallelizing transformations of array-intensive programs 3

Data-race detection: The missing piece for an end-to-end sem...

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3rd ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming, ARRAY 2016

作者： Banerjee, Kunal Banerjee, Soumyadip Sarkar, Santonu Dept of Computer Sc and Engg IIT Kharagpur India Dept of CSIS BITS Pilani-Goa India Intel Parallel Computing Lab. Bangalore India

ISBN: (纸本)9781450343848

The parallelizing transformation (hand-crafted or compiler-assisted) is error prone as it is often performed without verifying any semantic equivalence with the sequential counterpart. Even when the parallel program can be proved to be semantically equivalent with its corresponding sequential program, detecting data-race conditions in the parallel program remains a challenge. In this paper, we propose a formal verification approach that can detect data-race conditions while verifying the computational semantic equivalence of parallelizing loop transformations. We propose a coloured array data dependence graph (C-ADDG) based modeling of a program for verification of program equivalence as well as data-race condition detection across parallel loops. We have tested our tool on a set of Rodinia and PLuTo+ benchmarks and shown that our method is sound, whereby the method does not produce any false-positive program equivalence or data-race condition. © 2016 ACM.

关键词： Software testing

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CloudRAMSort: Fast and efficient large-scale distributed RAM sort on shared-nothing cluster 12

CloudRAMSort: Fast and efficient large-scale distributed RAM...

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2012 ACM SIGMOD International Conference on Management of Data, SIGMOD '12

作者： Kim, Changkyu Park, Jongsoo Satish, Nadathur Lee, Hongrae Dubey, Pradeep Chhugani, Jatin Parallel Computing Lab. Intel Labs. Santa Clara CA United States Structured Data Group Google Research Mountain View United States

ISBN: (纸本)9781450312479

Sorting is a fundamental kernel used in many database operations. The total memory available across cloud computers is now sufficient to store even hundreds of terabytes of data in-memory. Applications requiring high-speed data analysis typically use in-memory sorting. The two most important factors in designing a high-speed in-memory sorting system are the single-node sorting performance and inter-node communication. In this paper, we present CloudRAMSort, a fast and efficient system for large-scale distributed sorting on shared-nothing clusters. CloudRAMSort performs multi-node optimizations by carefully overlapping computation with inter-node communication. The system uses a dynamic multi-stage random sampling approach for improved load-balancing between nodes. CloudRAMSort maximizes per-node efficiency by exploiting modern architectural features such as multiple cores and SIMD (Single-Instruction Multiple Data) units. This holistic combination results in the highest performing sorting performance on distributed shared-nothing platforms. CloudRAMSort sorts 1 Terabyte (TB) of data in 4.6 seconds on a 256-node Xeon X5680 cluster called the Intel Endeavor system. CloudRAMSort also performs well on heavily skewed input distributions, sorting 1 TB of data generated using Zipf distribution in less than 5 seconds. We also provide a detailed analytical model that accurately projects (within avg. 7%) the performance of CloudRAMSort with varying tuple sizes and interconnect bandwidths. Our analytical model serves as a useful tool to analyze performance bottlenecks on current systems and project performance with future architectural advances. With architectural trends of increasing number of cores, bandwidth, SIMD width, cache-sizes, and interconnect bandwidth, we believe CloudRAMSort would be the system of choice for distributed sorting of large-scale in-memory data of current and future systems. © 2012 ACM.

关键词： Sorting

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