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Streaming Tiles: Flexible Implementation of Convolution Neural Networks Inference on Manycore Architectures 32

Streaming Tiles: Flexible Implementation of Convolution Neur...

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27th international Heterogeneity in Computing Workshop in conjunction with 32nd IEEE international parallel and distributed processing symposium (IPDPS)

作者： Rezk, Nesma M. Ul-Abdin, Zain Purnaprajna, Madhura Halmstad Univ Sch Informat Technol Halmstad Sweden Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept CSE Bengaluru India

ISBN: (纸本)9781538655559

Convolution neural networks (CNN) are extensively used for deep learning applications such as image recognition and computer vision. The convolution module of these networks is highly compute-intensive. Having an efficient implementation of the convolution module enables realizing the inference part of the neural network on embedded platforms. Low precision parameters require less memory, less computation time, and less power consumption while achieving high classification accuracy. Furthermore, streaming the data over parallefized processing units saves a considerable amount of memory, which is a key concern in memory constrained embedded platforms. In this paper, we explore the design space for streamed CNN on Epiphany manycore architecture using varying precisions for weights (ranging from binary to 32-bit). Both AlexNet and GoogleNet are explored for two different memory sizes of Epiphany cores. We are able to achieve competitive performance for both Alexnet and GoogleNet with respect to emerging many-cores. Furthermore, the effects of different design choices in terms of precision, memory size, and the number of cores are evaluated by applying the proposed method.

关键词： manycores CNN stream processing embedded systems

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Auto-tuning Streamed applications on Intel Xeon Phi 32

Auto-tuning Streamed Applications on Intel Xeon Phi

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27th international Heterogeneity in Computing Workshop in conjunction with 32nd IEEE international parallel and distributed processing symposium (IPDPS)

作者： Zhang, Peng Fang, Jianbin Tang, Tao Yang, Canqun Wang, Zheng Natl Univ Def Technol Coll Comp Compiler Lab Changsha Hunan Peoples R China Univ Lancaster Sch Comp & Commun MetaLab Lancaster England

ISBN: (纸本)9781538643686

Many-core accelerators, as represented by the XeonPhi coprocessors and GPGPUs, allow software to exploit spatial and temporal sharing of computing resources to improve the overall system performance. To unlock this performance potential requires software to effectively partition the hardware resource to maximize the overlap between host-device communication and accelerator computation, and to match the granularity of task parallelism to the resource partition. However, determining the right resource partition and task parallelism on a per program, per dataset basis is challenging. This is because the number of possible solutions is huge, and the benefit of choosing the right solution may be large, but mistakes can seriously hurt the performance. In this paper, we present an automatic approach to determine the hardware resource partition and the task granularity for any given streamed application, targeting the Intel XeonPhi architecture. Instead of hand-crafting the heuristic for which the process will have to repeat for each hardware generation, we employ machine learning techniques to automatically learn it. We achieve this by first learning a predictive model offline using training programs;we then use the learned model to predict the resource partition and task granularity for any unseen programs at runtime. We apply our approach to 23 representative parallel applications and evaluate it on a CPU-XeonPhi mixed heterogenous many-core platform. Our approach achieves, on average, a 1.6x (upto 5.6x) speedup, which translates to 94.5% of the performance delivered by a theoretically perfect predictor.

关键词： Heterogeneous computing parallelism Performance Tuning Machine learning

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SELECT: A distributed Publish/Subscribe Notification System for Online Social Networks 32

SELECT: A Distributed Publish/Subscribe Notification System ...

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32nd IEEE international parallel and distributed processing symposium (IPDPS)

作者： Apolonia, Nuno Antaris, Stefanos Girdzijauskas, Sarunas Pallis, George Dikaiakos, Marios Univ Politecn Cataluna Barcelona Spain Royal Inst Technol Stockholm Sweden Univ Cyprus Nicosia Cyprus

ISBN: (纸本)9781538643686

Publish/subscribe (pub/sub) mechanisms constitute an attractive communication paradigm in the design of large-scale notification systems for Online Social Networks (OSNs). To accommodate the large-scale workloads of notifications produced by OSNs, pub/sub mechanisms require thousands of servers distributed on different data centers all over the world, incurring large overheads. To eliminate the pub/sub resources used, we propose SELECT - a distributed pub/sub social notification system over peer-to-peer (P2P) networks. SELECT organizes the peers on a ring topology and provides an adaptive P2P connection establishment algorithm where each peer identifies the number of connections required, based on the social structure and user availability. This allows to propagate messages to the social friends of the users using a reduced number of hops. The presented algorithm is an efficient heuristic to an NPhard problem which maps workload graphs to structured P2P overlays inducing overall, close to theoretical, minimal number of messages. Experiments show that SELECT reduces the number of relay nodes up to 89% versus the state-of-the-art pub/sub notification systems. Additionally, we demonstrate the advantage of SELECT against socially-aware P2P overlay networks and show that the communication between two socially connected peers is reduced on average by at least 64% hops, while achieving 100% communication availability even under high churn.

关键词： Publish/Subscribe P2P Network Social Networks

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A Fast Scalable Implicit Solver with Concentrated Computation for Nonlinear Time-evolution Problems on Low-order Unstructured Finite Elements 32

A Fast Scalable Implicit Solver with Concentrated Computatio...

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27th international Heterogeneity in Computing Workshop in conjunction with 32nd IEEE international parallel and distributed processing symposium (IPDPS)

作者： Ichimura, Tsuyoshi Fujita, Kohei Horikoshi, Masashi Meadows, Larry Nakajima, Kengo Yamaguchi, Takuma Koyama, Kentaro Inoue, Hikaru Naruse, Akira Katsushima, Keisuke Hori, Muneo Lalith, Maddegedara Univ Tokyo Earthquake Res Inst Tokyo Japan Univ Tokyo Dept Civil Engn Tokyo Japan RIKEN Adv Inst Computat Sci Kobe Hyogo Japan Intel KK Software & Solut Grp Tokyo Japan Intel Corp Data Ctr Grp Hillsboro OR 97124 USA Univ Tokyo Ctr Informat Technol Tokyo Japan Fujitsu Ltd Frontier Comp Ctr Tokyo Japan NVIDIA Corp Tokyo Japan

ISBN: (纸本)9781538643686

Many supercomputers are shifting to architectures with low B (byte/s;memory transfer capability) per F (FLOPS capability) ratios. However, utilizing increased F is difficult for applications that inherently require large B. Targeting an implicit unstructured low-order finite-element analysis solver, which typically requires large B, we have developed a concentrated computation algorithm that yields significant performance improvements on low B/F supercomputers. 35.7% peak performance was achieved for a sparse matrix-vector multiplication kernel, and 15.6% peak performance was achieved for the whole solver on the second generation Xeon Phi-based Oakforest-PACS. This is 5.02 times faster than (and 6.90 times the peak performance of) the state-of-the-art solver (the SC14 Gordon Bell finalist solver). On Oakforest-PACS, the proposed solver was approximately 2.42 times faster than the state-of-the-art solver running on the K computer. The proposed approach has implications for systems and applications and is expected to have significant impact on various fields that use finite-element methods for nonlinear time-evolution problems.

关键词： Fast Scalable Implicit Solver for Nonlinear Time-evolution Problems Low-order Unstructured Finite Elements Concentrated Computation Time-parallel

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BabelFlow: An Embedded Domain Specific Language for parallel Analysis and Visualization 32

BabelFlow: An Embedded Domain Specific Language for Parallel...

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32nd IEEE international parallel and distributed processing symposium (IPDPS)

作者： Petruzza, Steve Treichler, Sean Pascucci, Valerio Bremer, Peer-Timo Univ Utah Salt Lake City UT 84112 USA Stanford Univ Stanford CA 94305 USA Lawrence Livermore Natl Lab Livermore CA USA

ISBN: (纸本)9781538643686

The rapid growth in simulation data requires large-scale parallel implementations of scientific analysis and visualization algorithms, both to produce results within an acceptable timeframe and to enable in situ deployment. However, efficient and scalable implementations, especially of more complex analysis approaches, require not only advanced algorithms, but also an in-depth knowledge of the underlying runtime. Furthermore, different machine configurations and different applications may favor different runtimes, i.e., MPI vs Charm++ vs Legion, etc., and different hardware architectures. This diversity makes developing and maintaining a broadly applicable analysis software infrastructure challenging. We address some of these problems by explicitly separating the implementation of individual tasks of an algorithm from the dataflow connecting these tasks. In particular, we present an embedded domain specific language (EDSL) to describe algorithms using a new task graph abstraction. This task graph is then executed on top of one of several available runtimes (MPI, Charm++, Legion) using a thin layer of library calls. We demonstrate the flexibility and performance of this approach using three different large scale analysis and visualization use cases, i.e., topological analysis, rendering and compositing dataflow, and image registration of large microscopy scans. Despite the unavoidable overheads of a generic solution, our approach demonstrates performance portability at scale, and, in some cases, outperforms hand-optimized implementations.

关键词： Embedded DSL User productivity In-situ analysis Simulation runtime systems Programming models

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Data-Locality Aware Dynamic Schedulers for Independent Tasks with Replicated Inputs 32

Data-Locality Aware Dynamic Schedulers for Independent Tasks...

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32nd IEEE international parallel and distributed processing symposium (IPDPS)

作者： Beaumont, Olivier Lambert, Thomas Marchal, Loris Thomas, Bastien INRIA Talence France Univ Bordeaux Talence France Univ Manchester Manchester Lancs England ENS Lyon Lyon France ENS Rennes Ker Lann France

ISBN: (纸本)9781538655559

In this paper we concentrate on a crucial parameter for efficiency in Big Data and HPC applications: data locality. We focus on the scheduling of a set of independant tasks, each depending on an input file. We assume that each of these input files has been replicated several times and placed in local storage of different nodes of a cluster, similarly of what we can find on HDFS system for example. We consider two optimization problems, related to the two natural metrics: makespan optimization (under the constraint that only local tasks are allowed) and communication optimization (under the constraint of never letting a processor idle in order to optimize makespan). For both problems we investigate the performance of dynamic schedulers, in particular the basic greedy algorithm we can for example find in the default MapReduce scheduler. First we theoretically study its performance, with probabilistic models, and provide a lower bound for communication metric and asymptotic behaviour for both metrics. Second we propose simulations based on traces from a Hadoop cluster to compare the different dynamic schedulers and assess the expected behaviour obtained with the theoretical study.

关键词： Task analysis Measurement Dynamic scheduling distributed databases Runtime Big Data Optimization"

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Proceedings - 2018 IEEE 32nd international parallel and distributed processing symposium, IPDPS 2018

Proceedings - 2018 IEEE 32nd International Parallel and Dist...

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32nd IEEE international parallel and distributed processing symposium, IPDPS 2018

ISBN: (纸本)9781538643686

The proceedings contain 113 papers. The topics discussed include: optimizing parallel graph connectivity computation via subgraph sampling;a parallel algorithm for Bayesian network inference using arithmetic circuits;cataloging the visible universe through Bayesian inference at petascale;efficient, parallel at-scale correlation analysis for atom probe tomography on hybrid architectures;a fast and massively-parallel inverse solver for multiple-scattering tomographic image reconstruction;real-time massively distributed multi-object adaptive optics simulations for the European extremely large telescope;performance isolation of data-intensive scale-out applications in a multi-tenant cloud;scalable data resilience for in-memory data staging;and performance and scalability of lightweight multi-kernel based operating systems.

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A distributed User Authentication Mechanism for 6TiSCH Protocol 2

A Distributed User Authentication Mechanism for 6TiSCH Proto...

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2nd international symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： Aydin, Hakan Gormus, Sedat Karadeniz Tech Univ Bilgisayar Muhendisligi Trabzon Turkey

ISBN: (纸本)9781538641842

The Internet of Things (IoT) has become a popular research topic in recent times. IoT networks enable billions of small devices to be integrated into the Internet and numerous applications ranging from automation of cities to home based health solutions for the elderly population. IoT network with numerous application areas also includes difficulties such as reliability, low power and low latency. Such low-powered Internet-based devices need to be configured with the security requirements needed to address the security problems that the Internet creates. In addition, security requirements must address such unique challenges through limited processing power and through a micro controller that consumes as little power as possible. This paper presents a plug-in to the secure boot mechanism of the 6TiSCH protocol that is deployed in the trusted nodes of the IoT network to provide an efficient boot process for authentication keys. By using the proposed approach, we aim to reduce the communication load of the standard 6TiSCH authentication mechanism and increase the energy efficiency of the network by keeping the authentication tokens at the edge of the IoT network.

关键词： IoT 6TiSCH Bootstrapping Authentication

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Tacho: Memory-Scalable Task parallel Sparse Cholesky Factorization 32

Tacho: Memory-Scalable Task Parallel Sparse Cholesky Factori...

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27th international Heterogeneity in Computing Workshop in conjunction with 32nd IEEE international parallel and distributed processing symposium (IPDPS)

作者： Kim, Kyungjoo Edwards, H. Carter Rajamanickam, Sivasankaran Sandia Natl Labs Ctr Comp Res POB 5800 Albuquerque NM 87185 USA

ISBN: (纸本)9781538655559

We present a memory-scalable, parallel, sparse multifrontal solver for solving symmetric postive-definite systems arising in scientific and engineering applications. Factorizing sparse matrices requires memory for both the computed factors and the temporary workspaces for computing each frontal matrix - a data structure commonly used within multifrontal methods. To factorize multiple frontal matrices in parallel, the conventional approach is to allocate a uniform workspace for each hardware thread. In the manycore era, this results in increasing memory usage proportional to the number of hardware threads. We remedy this problem by using dynamic task parallelism with a scalable memory pool. Tasks are spawned while traversing an assembly tree and executed after their dependences are satisfied. We also use an idea to respawn the tasks when certain conditions are not met. Temporary workspace for frontal matrices in each task is allocated from a memory pool designed by us. If the requested memory space is not available in the memory pool, the task is respawned to yield the hardware thread to execute other tasks. The respawned task is executed after high priority tasks are executed. This approach allows to have robust parallel performance within a bounded memory space. Experimental results demonstrate the merits of our implementation on Intel multicore and manycore architectures.

关键词： Kokkos Task parallelism Memory Pool Sparse Direct Method Cholesky Multifrontal

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Empowering flexible and scalable high performance architectures with embedded photonics

Empowering flexible and scalable high performance architectu...

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32nd IEEE international parallel and distributed processing symposium (IPDPS)

作者： Bergman, Keren Columbia Univ Elect Engn New York NY 10027 USA Columbia Univ Columbia Nano Initiat New York NY 10027 USA

ISBN: (纸本)9781538643686

The recent explosive growth in data analytics applications that rely on machine and deep learning techniques are seismically changing the landscape of high performance architectures. These techniques rely on graphics processing units (GPU) and manycore (CPU) technologies whose need for intense performance is pushing current interconnect networks to their limits. Driven by these applications, the execution performance along with the energy consumption of massive parallel systems is increasingly determined by how data is moved among the numerous compute and memory resources. Embedded photonic interconnect technologies can address critical data-movement challenges by delivering higher communication bandwidth densities at significantly improved energy efficiencies. New disaggregated architectures enabled by embedded photonics and optical bandwidth steering can reduce the system-wide energy consumption.

关键词： Photonics Computer architecture Graphics processing units Energy consumption Bandwidth Biomedical optical imaging Optical network units"

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