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Pareto optimal design space exploration for accelerated CNN on FPGA 33

Pareto optimal design space exploration for accelerated CNN ...

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33rd ieee international parallel and distributed processing symposium (IPDPS)

作者： Reggiani, Enrico Rabozzi, Marco Nestorov, Anna Maria Scolari, Alberto Stornaiulo, Luca Santambrogio, Marco D. Politecn Milan Milan Italy

ISBN: (纸本)9781538655559

Convolutional Neural Networks (CNNs) are at the base of many applications, both in embedded and in serverclass contexts. While Graphics processing Units (GPUs) are predominantly used for training, solutions for inference often rely on Field Programmable Gate Arrays (FPGAs) since they are more flexible and cost-efficient in many scenarios. However, existing approaches fall short to accomplish several conflicting goals, like efficiently using resources on multiple platforms while retaining deep configurability and allowing a quick Design Space Exploration (DSE) towards the best solution. This paper proposes a solution composed of highly configurable kernels designed for resources time-sharing with an analytical model of their resource/performance characteristics. Building on such models, we propose an Integer Linear Programming (ILP)-based approach to effectively identify pareto optimal kernel configurations in terms of throughput and resource consumption. We evaluate our DSE on two state-of-the-art CNNs, showing how it identifies hundreds of pareto optimal solutions in less than a minute. Guided from the DSE configurations of the AlexNet network, we quickly identified a candidate design for a Xilinx Virtex-7 XC7VX485T FPGA and achieved a peak throughput of 4.05 ms per image, while we measured a maximum estimation error of 6.69% with respect to the proposed analytical models.

关键词： Field programmable gate arrays Convolution parallel processing Computer architecture Kernel Acceleration Pareto optimization

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Classifying Pedagogical Material to Improve Adoption of parallel and distributed Computing Topics 33

Classifying Pedagogical Material to Improve Adoption of Para...

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33rd ieee international parallel and distributed processing symposium (IPDPS)

作者： Goncharow, Alec Boekelheide, Anna Mcquaigue, Matthew Burlinson, David Saule, Erik Subramanian, Kalpathi Payton, Jamie UNC Charlotte Comp Sci Charlotte NC 28223 USA Temple Univ Comp & Informat Sci Philadelphia PA 19122 USA

ISBN: (纸本)9781538655559

The NSF/ieee-TCPP parallel and distributed Computing curriculum guidelines released in 2012 (PDC12) is an effort to bring more parallel computing education to early computer science courses. It has been moderately successful, with the inclusion of some PDC topics in the ACM/ieee Computer Science curriculum guidelines in 2013 (CS13) and some coverage of topics in early CS courses in some universities in the U.S. and around the world. A reason often cited for the lack of a broader adoption is the difficulty for instructors who are not already knowledgable in PDC topics to learn how to teach those topics and align their learning objectives with early CS courses. There have been attempts at bringing textbook chapters, lecture slides, assignments, and demos to the hands of the instructors of early CS classes. However, the effort required to plow through all the available materials and figure out what is relevant to a particular class is daunting. This paper argues that classifying pedagogical materials against the CS13 guidelines and the PDC12 guidelines can provide the means necessary to reduce the burden of adoption for instructors. In this paper, we present CAR-CS, a system that can be used to categorize pedagogical materials according to well-known and established curricular guidelines and show that CAR-CS can be leveraged 1) by PDC experts to identify topics for which pedagogical material does not exist and that should be developed, 2) by instructors of early CS courses to find materials that are similar to the one that they use but that also cover PDC topics, 3) by instructors to check the topics that a course currently covers and those it does not cover.

关键词： PDC curriculum exemplar classification adoption of PDC in early CS courses

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Neural Network Frameworks. Comparison on Public Transportation Prediction 33

Neural Network Frameworks. Comparison on Public Transportati...

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33rd ieee international parallel and distributed processing symposium (IPDPS)

作者： Heghedus, Cristina Chakravorty, Antorweep Rong, Chunming Univ Stavanger Dept Comp Sci & Elect Engn Stavanger Norway

ISBN: (纸本)9781538655559

In the era of Internet of Things (IoT), huge amount of data is being produced, by humans, sensors and machines. Thus prediction, modeling and decision making, in the majority of fields, have become highly data driven. In the vast field of Energy Informatics, IoT devices include video/photo cameras, smart meter (recording electricity usage), permanent downhole gauges (PDG - used in petroleum industry), GPS devices (used in transportation), etc. Constant research is being conducted on topics such as electricity forecasting, renewable energy production, public transit optimization and prediction, oil and gas production, solar radiation, wind power and so on. The interest in this paper is centered around public transportation data. Related works, on all kinds of Energy tasks, are meticulous and fruitful, always showing improvements. However, the topic is not by far exhausted, due to the constant evolution of technology and emergence of new methods. Implementing a model for forecasting, optimization or other tasks, is user and field dependent. In this work, a parallel between two high level implementation methods, Tensorflow and PyTorch, is presented, highlighting the advantages of each, regarding computational requirements, learning, parallel programming, data parallelism, accuracy, etc. Four Neural Networks are tested for this scope, two Machine Learning and two Deep Learning models, implemented in Tensorflow and in PyTorch. The results show that models implemented in Tensorflow are slightly more accurate, however, models in PyTorch are faster in training and easier to implement.

关键词： Machine Learning PyTorch Tensorflow parallel training GPU programming Public transportation

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Accelerating Sequence Alignment to Graphs 33

Accelerating Sequence Alignment to Graphs

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33rd ieee international parallel and distributed processing symposium (IPDPS)

作者： Jain, Chirag Misra, Sanchit Zhang, Haowen Dilthey, Alexander Aluru, Srinivas Georgia Inst Technol Sch Computat Sci & Engr Atlanta GA 30332 USA Intel Corp Parallel Comp Lab Bangalore Karnataka India Univ Hosp Dusseldorf Inst Med Microbiol Dusseldorf Germany

ISBN: (纸本)9781728112466

Aligning DNA sequences to an annotated reference is a key step for genotyping in biology. Recent scientific studies have demonstrated improved inference by aligning reads to a variation graph, i.e., a reference sequence augmented with known genetic variations. Given a variation graph in the form of a directed acyclic string graph, the sequence to graph alignment problem seeks to find the best matching path in the graph for an input query sequence. Solving this problem exactly using a sequential dynamic programming algorithm takes quadratic time in terms of the graph size and query length, making it difficult to scale to high throughput DNA sequencing data. In this work, we propose the first parallel algorithm for computing sequence to graph alignments that leverages multiple cores and single-instruction multiple-data (SIMD) operations. We take advantage of the available inter-task parallelism, and provide a novel blocked approach to compute the score matrix while ensuring high memory locality. Using a 48-core Intel Xeon Skylake processor, the proposed algorithm achieves peak performance of 317 billion cell updates per second (GCUPS), and demonstrates near linear weak and strong scaling on up to 48 cores. It delivers significant performance gains compared to existing algorithms, and results in run-time reduction from multiple days to three hours for the problem of optimally aligning high coverage long (PacBio/ONT) or short (Illumina) DNA reads to an MHC human variation graph containing 10 million vertices.

关键词： parallel algorithm architecture-aware optimization variation graphs sequence alignment genomics

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CuSP: A Customizable Streaming Edge Partitioner for distributed Graph Analytics 33

CuSP: A Customizable Streaming Edge Partitioner for Distribu...

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33rd ieee international parallel and distributed processing symposium (IPDPS)

作者： Hoang, Loc Dathathri, Roshan Gill, Gurbinder Pingali, Keshav Univ Texas Austin Dept Comp Sci Austin TX 78712 USA

ISBN: (纸本)9781728112466

Graph analytics systems must analyze graphs with billions of vertices and edges which require several terabytes of storage. distributed-memory clusters are often used for analyzing such large graphs since the main memory of a single machine is usually restricted to a few hundreds of gigabytes. This requires partitioning the graph among the machines in the cluster. Existing graph analytics systems usually come with a built-in partitioner that incorporates a particular partitioning policy, but the best partitioning policy is dependent on the algorithm, input graph, and platform. Therefore, built-in partitioners are not sufficiently flexible. Stand-alone graph partitioners are available, but they too implement only a small number of partitioning policies. This paper presents CuSP, a fast streaming edge partitioning framework which permits users to specify the desired partitioning policy at a high level of abstraction and generates high-quality graph partitions fast. For example, it can partition wdc12, the largest publicly available web-crawl graph, with 4 billion vertices and 129 billion edges, in under 2 minutes for clusters with 128 machines. Our experiments show that it can produce quality partitions 6x faster on average than the state-of-the-art stand-alone partitioner in the literature while supporting a wider range of partitioning policies.

关键词： Graph analytics graph partitioning streaming partitioners distributed-memory computing

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GraphTinker: A High Performance Data Structure for Dynamic Graph processing 33

GraphTinker: A High Performance Data Structure for Dynamic G...

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33rd ieee international parallel and distributed processing symposium (IPDPS)

作者： Jaiyeoba, Wole Skadron, Kevin Univ Virginia Dept Comp Sci Charlottesville VA 22903 USA

ISBN: (纸本)9781728112466

Interestin high performance analytics for dynamic (constantly evolving) graphs has been on the rise in the last decade, especially due to the prevalence and rapid growth of social networks today. The current state-of-the art data structures for dynamic graph processing rely on the adjacency list model of edgeblocks in updating graphs. This model suffers from long probe distances when following edges, leading to poor update throughputs. Furthermore, both current graph processing models the static model that requires reprocessing the entire graph after every batch update, and the incremental model in which only the affected subset of edges need to be processed suffer drawbacks. In this paper, we present GraphTinker, a new, more scalable graph data structure for dynamic graphs. It uses a new hashing scheme to reduce probe distance and improve edge-update performance. It also better compacts edge data. These innovations improve performance for graph updates as well as graph analytics. In addition, we present a hybrid engine which improves the performance of dynamic graph processing by automatically selecting the most optimal execution model (static vs. incremental) for every iteration, surpassing the performance of both. Our evaluations of GraphTinker shows a throughput improvement of up to 3.3X compared to the state-of-the-art data structure (STINGER) when used for graph updates. GraphTinker also demonstrates a performance improvement of up to 10X over STINGER when used to run graph analytics algorithms. In addition, our hybrid engine demonstrates up to 2X improvement over the incremental-compute model and up to 3X improvement over the static model.

关键词： Probes Computational modeling Throughput Analytical models Data models Arrays

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Token Based parallel processing Retrial Queueing System with a Probabilistic Joining Strategy for Priority Customers 23rd

Token Based Parallel Processing Retrial Queueing System with...

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23rd international Conference on distributed Computer and Communication Networks: Control, Computation, Communications , DCCN 2020

作者： Babu, Dhanya Joshua, V.C. Krishnamoorthy, A. Department of Mathematics CMS College Kottayam Kerala India

ISBN: (纸本)9783030662417

We consider a single server retrial queueing system with parallel queues of ordinary and priority customers. Priority customers join the queues according to a probabilistic joining strategy until the ordinary queue is full. We also assume that there is a waiting room for the blocked ordinary customers and they make retrials from there, to enter into the ordinary queue. Priority customers on arrival, seeing both ordinary and priority queue as full loss the system forever. Customers receive service on the basis of a token system. Ordinary customers, upon arrival finding the ordinary queue as full, enter into the orbit. Ordinary customers make their retrials from the orbit in an exponential duration of time intervals to enter into the ordinary queue. Ordinary customer from the orbit make their retrials in an exponential duration of time intervals. Whenever the queue size of ordinary queue is less than N then the ordinary customer in the orbit successfully enter into the ordinary queue. Two types of customers arrive according to Marked Markovian Arrival Process (MMAP). Service times are assumed to follow phase type distributions. Steady-state analysis of the model is done. Some system characteristics are evaluated. © 2020, Springer Nature Switzerland AG.

关键词： Sales

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iez: Resource Contention Aware Load Balancing for Large-Scale parallel File Systems 33

iez: Resource Contention Aware Load Balancing for Large-Scal...

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33rd ieee international parallel and distributed processing symposium (IPDPS)

作者： Wadhwa, Bharti Paul, Arnab K. Neuwirth, Sarah Wang, Feiyi Oral, Sarp Butt, Ali R. Bernard, Jon Cameron, Kirk W. Virginia Tech Blacksburg VA 24061 USA Heidelberg Univ Heidelberg Germany Oak Ridge Natl Lab Oak Ridge TN USA

ISBN: (纸本)9781728112466

parallel I/O performance is crucial to sustaining scientific applications on large-scale High-Performance Computing (HPC) systems. However, I/O load imbalance in the underlying distributed and shared storage systems can significantly reduce overall application performance. There are two conflicting challenges to mitigate this load imbalance: (i) optimizing system-wide data placement to maximize the bandwidth advantages of distributed storage servers, i.e., allocating I/O resources efficiently across applications and job runs;and (ii) optimizing client-centric data movement to minimize I/O load request latency between clients and servers, i.e., allocating I/O resources efficiently in service to a single application and job run. Moreover, existing approaches that require application changes limit wide-spread adoption in commercial or proprietary deployments. We propose iez, an "end-to-end control plane" where clients transparently and adaptively write to a set of selected I/O servers to achieve balanced data placement. Our control plane leverages real-time load information for distributed storage server global data placement while our design model leverages trace-based optimization techniques to minimize I/O load request latency between clients and servers. We evaluate our proposed system on an experimental cluster for two common use cases: synthetic I/O benchmark IOR for large sequential writes and a scientific application I/O kernel, HACC-I/O. Results show read and write performance improvements of up to 34% and 32%, respectively, compared to the state of the art.

关键词： Servers distributed databases Load management Benchmark testing Resource management Libraries Metadata

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Practically Efficient Scheduler for Minimizing Average Flow Time of parallel Jobs 33

Practically Efficient Scheduler for Minimizing Average Flow ...

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33rd ieee international parallel and distributed processing symposium (IPDPS)

作者： Agrawal, Kunal Lee, I-Ting Angelina Li, Jing Lu, Kefu Moseley, Benjamin Washington Univ St Louis MO 63110 USA New Jersey Inst Technol Newark NJ 07102 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781728112466

Many algorithms have been proposed to efficiently schedule parallel jobs on a multicore and/or multiprocessor machine to minimize average flow time, and the complexity of the problem is well understood. In practice, the problem is far from being understood. A reason for the gap between theory and practice is that all theoretical algorithms have prohibitive overheads in actual implementation including using many preemptions. One of the flagship successes of scheduling theory is the work-stealing scheduler. Work-stealing is used for optimizing the flow time of a single parallel job executing on a single machine with multiple cores and has a strong performance in theory and in practice. Consequently, it is implemented in almost all parallel runtime systems. This paper seeks to bridge theory and practice for scheduling parallel jobs that arrive online, by introducing an adaptation of the work-stealing scheduler for average flow time. The new algorithm distributed Random Equi-Partition (DREP) has strong practical and theoretical performance. Practically, the algorithm has the following advantages: (1) it is non-clairvoyant;(2) all processors make scheduling decisions in a decentralized manner requiring minimal synchronization and communications;and (3) it requires a small and bounded number of preemptions. Theoretically, we prove that DREP is (4 + epsilon)-speed O(1/epsilon(3))-competitive for average flow time. We have empirically evaluated DREP using both simulations and actual implementation by modifying the Cilk Plus work-stealing runtime system. The evaluation results show that DREP performs well compared to other scheduling strategies, including those that are theoretically good but cannot be faithfully implemented in practice.

关键词： parallel scheduling online scheduling work stealing average flow time

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FALCON: Efficient Designs for Zero-copy MPI Datatype processing on Emerging Architectures 33

FALCON: Efficient Designs for Zero-copy MPI Datatype Process...

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33rd ieee international parallel and distributed processing symposium (IPDPS)

作者： Hashmi, Jahanzeb Maqbool Chakraborty, Sourav Bayatpour, Mohammadreza Subramoni, Hari Panda, Dhabaleswar K. (DK) Ohio State Univ Columbus OH 43210 USA

ISBN: (纸本)9781728112466

Derived datatypes are commonly used in MPI applications to exchange non-contiguous data among processes. However, state-of-the-art MPI libraries do not offer efficient processing of derived datatypes and often rely on packing and unpacking the data at the sender and the receiver processes. This approach incurs the cost of extra copies and increases overall communication latency. While zero-copy communication schemes have been proposed for contiguous data, applying such techniques to non-contiguous data transfers bring forth several new challenges. In this work, we address these challenges and propose FALCON Fast and Low-overhead Communication designs for intra-node MPI derived datatypes processing. We show that the memory layouts translation of derived datatypes introduce significant overheads in the communication path and propose novel solutions to mitigate such bottlenecks. We also find that the current MPI datatype routines cannot fully take advantage of the zero-copy mechanisms, and propose enhancements to the MPI standard to address these limitations. The experimental evaluations show that our proposed designs achieve up to 3 times improved intra-node communication latency and bandwidth over state-of-the-art MPI libraries. We also evaluate our designs with communication kernels of popular scientific applications such as MILC, WRF, NAS_MG, and 3D-Stencil on three different multi-/many-core architectures and show up to 5.5 times improvement over state-of-the-art designs employed by production MPI libraries.

关键词： Derived Datatype Zero-copy Data Marshaling Multi-/Many-core MPI HPC

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