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Evaluating I/O Acceleration Mechanisms of SX-Aurora TSUBASA

Evaluating I/O Acceleration Mechanisms of SX-Aurora TSUBASA

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Sasaki, Yuta Ishizuka, Ayumu Agung, Mulya Takizawa, Hiroyuki Tohoku Univ Grad Sch Informat Sci Sendai Miyagi Japan Tohoku Univ Cybersci Ctr Aoba Ku 6-3 Aramaki Aza Aoba Sendai Miyagi 9808578 Japan

ISBN: (纸本)9781665435772

In a heterogeneous computing system, different kinds of processors might need to be involved in the execution of a file I/O operation. Since NEC SX-Aurora TSUBASA is one such system, two I/O acceleration mechanisms are offered to reduce the data transfer overheads among the processors for a file I/O operation. This paper first investigates the effects of the two mechanisms on the I/O performance of SX-Aurora TSUBASA. Considering the results, proper use of the two mechanisms is discussed via a real-world application of flood damage estimation. These results clearly demonstrate the demand for auto-tuning, i.e., adaptively selecting either of the two mechanisms with considering application behaviors and system configuration.

关键词： Performance evaluation distributed processing Program processors File systems Conferences Estimation Data transfer

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Integrating parallel, Tensor-Based computing in Agent-Based Simulators

Integrating Parallel, Tensor-Based Computing in Agent-Based ...

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international Workshop/symposium on distributed Simulation and Real-Time Applications (DS-RT)

作者： Nikitas M. Sgouros Department of Digital Systems University of Piraeus Piraeus Greece

The rise of deep learning methods has ignited interest in efficient hardware and software systems for tensor-based computing. A question worth investigating is whether other areas in computing can benefit as well from the power and increasing availability of such systems. To this end, we study the integration of tensor-based techniques in agent-based simulators. In particular, we describe methods for the representation as vectors of agent and edge attributes in agent networks and the compilation of rules governing agent behavior and their edge-based interactions into functions that can process and compute such attribute vectors in parallel. We describe a proof of concept implementation of such an idea in Politika, our Elixir-based web framework for concurrent, agent-based simulation. We discuss various simulation scenarios for our implementation and provide directions for future optimizations and integration pathways with concurrent simulation environments.

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Finer-LRU: A Scalable Page Management Scheme for HPC Manycore Architectures 35

Finer-LRU: A Scalable Page Management Scheme for HPC Manycor...

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Bang, Jiwoo Kim, Chungyong Kim, Sunggon Chen, Qichen Lee, Cheongjun Byun, Eun-Kyu Lee, Jaehwan Eom, Hyeonsang Seoul Natl Univ Dept Comp Sci & Engn Seoul South Korea Korea Aerosp Univ Dept Elect & Informat Engn Goyang South Korea Korea Inst Sci & Technol Informat Div Natl Supercomp Daejeon South Korea

ISBN: (纸本)9781665440660

In HPC systems, the increasing need for a higher level of concurrency has led to packing inure cores within a single chip. However, since multiple processes share memory space, the frequent access to resources in critical sections where only atomic operation has to be executed can result in poor performance. In this paper, we focus on reducing lock contention on the memory management system of an HPC manycore architecture. One of the critical sections causing severe lock contention in the I/O path is in the page management system, which uses multiple Least Recently Used (LRU) lists with a single lock instance. To solve this problem, we propose a Finer-LRU scheme, which optimizes the page reclamation process by splitting LRU lists into multiple sub-lists, each having its own lock instance. Our evaluation result shows that the Finer-LRU scheme can improve sequential write throughput by 57.93% and reduce latency by 98.94% compared to the baseline Linux kernel version 5.2.8 in the Intel Knights Landing (KNL) architecture.

关键词： High performance computing Manycore architecture Page reclamation process Fine-grained lock

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Autonomous Load Balancing in distributed Hash Tables Using Churn and the Sybil Attack

Autonomous Load Balancing in Distributed Hash Tables Using C...

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Rosen, Andrew Levin, Benjamin Bourgeois, Anu G. Temple Univ Dept Comp Sci Philadelphia PA 19122 USA Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA

ISBN: (纸本)9781665435772

distributed Hash Tables (DHTs) are an integral foundation for a variety of modern internet applications. In previous work, we have shown that DHTs can also be used as a means of organizing a large number of workers to tackle large-scale computing problems in a fault tolerant context. Whether a DHT is being used for file access or distributing a large-scale computing job, a cryptographic hash function is used to assign keys for nodes and data. Ideally, these would be uniformly distributed across the available range, thus evenly distributing the nodes and tasks. However, this is rarely the case in practice and as a result, the workload can become highly unbalanced. To address this issue, there have been numerous methods proposed for load balancing DHTs, but often they are a centralized approach. In this paper, we present four methods to autonomously balance the load of DHTs: 1) induced churn;2) random injection of Sybil Nodes;3) neighbor injection;and 4) invitation of nodes with low workloads. Each approach is completely decentralized, requiring minimal overhead, with individual nodes making decisions based only upon local information. What makes our approach unique is that the strategies rely on using the inherent churn in a DHT or by a variation of the Sybil attack to balance the workload. We simulate the four strategies on a Chord DHT and show they significantly rebalance the workload in a DHT. The strategy of randomly injecting virtual "Sybil" nodes performed the best in terms of balance and speedup.

关键词： distributed computing Decentralized Systems Peer-to-Peer Networks distributed Hash Tables Self-Organizing Networks Load-Balancing Sybil Attack

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GPU-Accelerated Tree-Search in Chapel Versus CUDA and HIP

GPU-Accelerated Tree-Search in Chapel Versus CUDA and HIP

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Guillaume Helbecque Ezhilmathi Krishnasamy Nouredine Melab Pascal Bouvry DCS-FSTM/SnT University of Luxembourg Luxembourg Université de Lille CNRS/CRIStAL UMR 9189 Centre Inria de l'Université de Lille France

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

In the context of exascale programming, the PGAS-based Chapel is among the rare languages targeting the holistic handling of high-performance computing issues including the productivity-aware harnessing of Nvidia and AMD GPUs. In this paper, we propose a pioneering proof-of-concept dealing with this latter issue in the context of tree-based exact optimization. Actually, we revisit the design and implementation of a generic multi-pool GPU-accelerated tree-search algorithm using Chapel. This algorithm is instantiated on the backtracking method and experimented on the N-Queens problem. For performance evaluation, the Chapel-based approach is compared to Nvidia CUDA and AMD HIP low-level counterparts. The reported results show that in a single-GPU setting, the high GPU abstraction of Chapel results in a loss of only 8% (resp. 16%) compared to CUDA (resp. HIP). In a multi-GPU setting, up to 80% (resp. 71%) of the baseline speedup is achieved for coarse-grained problem instances on Nvidia (resp. AMD) GPUs.

关键词： Performance evaluation distributed processing Backtracking High performance computing Conferences Graphics processing units Programming

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Modeling and Simulation of Heterogeneous Resources in the Cloud 20

Modeling and Simulation of Heterogeneous Resources in the Cl...

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IEEE 20th international symposium on Network computing and Applications (NCA)

作者： Loncar, Petra Univ Split Fac Elect Engn Mech Engn & Naval Architecture Split Croatia

ISBN: (纸本)9781665495509

Heterogenous cloud computing will take a significant role in addressing performance concerns at hyper-scale computing. New approaches are needed to manage the system complexity and to support an High-Performance computing (HPC) in the cloud. The large-scale modeling is associated with challenges in transitioning to a heterogeneous cloud with an emphasis on resource management. In this paper, the complex cloud system infrastructure of five heterogeneous data centers is modeled and simulated. The simulation is performed using a CloudSim simulator under the parallel workload. The idea is to connect data centers using a central broker for assigning tasks to data centers depending on their predefined settings and job requirements. The operation and performance of the system execution under a high load are shown. We consider this model optimal for distribution regarding complex data infrastructures. All assigned tasks were successfully performed on cloud data centers. Guidelines for future work are given.

关键词： cloud computing CloudSim distributed computing heterogenous cloud system simulation

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D²Graph: An Efficient and Unified Out-of-Core Graph computing Model 19

D<SUP>2</SUP>Graph: An Efficient and Unified Out-of-Core Gra...

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19th IEEE international symposium on parallel and distributed Processing with Applications (IEEE ISPA)

作者： Li, Baoke Cao, Cong Lu, Yuhai Liu, Yanbing Li, Baohui Fang, Binxing Fu, Jianhui Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Chinese Informat Technol Secur Evaluat Ctr Dept Syst Evaluat Beijing Peoples R China UESTC Guangdong Inst Elect & Informat Engn Dongguan Guangdong Peoples R China Shandong Inst Ind Technol Jinan Peoples R China

ISBN: (纸本)9781665435741

Graph computing recently receives intensive interests due to a wide range of needs to analyse big graphs in real time. Many single-machine out-of-core graph computing systems have been developed to process large-scale graphs by utilizing effective memory access techniques. The current systems either focus on graph compression or pay attention to disk scheduling strategies. In this paper, we propose an efficient and unified out-of-core graph computing model on a single machine named D(2)Graph which combines the differential storage strategy and the dynamic caching mechanism. D(2)Graph improves the spatial locality of graph data and the temporal locality of graph computing at the same time. The main contributions of our work lie in three aspects: firstly, we design a fine-grained differential storage(FGDS) strategy to reorganize graph data on disk;secondly, we propose a dynamic caching mechanism (DCM) to selectively load the necessary edge chunks;thirdly, we combine FGDS and DCM to construct D(2)Graph and evaluate it on five public graph data sets. What's more, to optimize the performance of D(2)Graph, we also make use of the multi-threaded parallel technology. A series of results show that D(2)Graph outperforms state-of-the-art out-of-core graph computing systems by 1.3x-17.9x.

关键词： High performance model graph computing differential storage dynamic cache

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Astra: Autonomous Serverless Analytics with Cost-Efficiency and QoS-Awareness 35

<i>Astra</i>: Autonomous Serverless Analytics with Cost-Effi...

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Jarachanthan, Jananie Chen, Li Xu, Fei Li, Bo Univ Louisiana Lafayette Lafayette LA 70504 USA East China Normal Univ Shanghai Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9781665440660

With the ability to simplify the code deployment with one-click upload and lightweight execution, serverless computing has emerged as a promising paradigm with increasing popularity. However, there remain open challenges when adapting data-intensive analytics applications to the serverless context, in which users of serverless analytics encounter with the difficulty in coordinating computation across different stages and provisioning resources in a large configuration space. This paper presents our design and implementation of Astra, which configures and orchestrates serverless analytics jobs in an autonomous manner, while taking into account flexibly-specified user requirements. Astra relies on the modeling of performance and cost which characterizes the intricate interplay among multi-dimensional factors (e.g., function memory size, degree of parallelism at each stage). We formulate an optimization problem based out user-specific requirements towards performance enhancement or cost reduction, and develop a set of algorithms based on graph theory to obtain optimal job execution. We deploy Astra in the AWS Lambda platform and conduct real-world experiments over three representative benchmarks with different scales. Results demonstrate that Astra can achieve the optimal execution decision for serverless analytics, by improving the performance of 21% to 60% under a given budget constraint, and resulting in a cost reduction of 20% to 80% without violating performance requirement, when compared with three baseline configuration algorithms.

关键词： Cloud computing serverless computing resource provisioning modeling optimization

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Optimizing Batched Small Matrix Multiplication on Multi-core DSP Architecture

Optimizing Batched Small Matrix Multiplication on Multi-core...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Xiaohan Zuo Chunhua Xiao Qinglin Wang Chen Shi College of Computer Science Chongqing University China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

General Matrix Multiplication (GEMM) is a critical computational operation in scientific computing and machine learning domains. While traditional GEMM performs well on large matrices, it is inefficient in terms of data transfer and computation for small matrices. Many High-Performance computing (HPC) tasks can be decomposed into large batches of small matrix multiplication operations. Multi-core Digital Signal Processors (DSPs) are commonly used to accelerate high-performance computing. We present a design for batched fusion small matrix multiplication (BFMM) tailored for multi-core DSP architecture. To address the inefficiencies and redundancy in storage and computational operations associated with batch small matrix multiplications, we designed several strategies. We design a matrix fusion concatenation strategy, an access coordination mechanism, and a mechanism for fragment aggregation. BFMM supports an efficient K-dimension multi-core parallelization strategy. The parameter constraint model makes BFMM highly portable. BFMM also includes a performance evaluation model that facilitates assessment and verification. Experimental results demonstrate that, compared to traditional GEMM (TGEMM) on multi-core DSP and traditional GEMM with concatenated data access (TGEMM Op), BFMM exhibits superior performance. For large batches of small matrices, our design achieves 1.21x to 18x higher performance than TGEMM Op on single-core DSP, while on multi-core DSP, it outperforms TGEMM Op by 1.14x to 18.1x.

关键词： Performance evaluation Scientific computing High performance computing Digital signal processors Redundancy Batch production systems Signal processing algorithms Computer architecture Machine learning Matrix decomposition

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Implementing An Outsourced Dual-Proxy Signing and Decryption Scheme in Mobile Cloud computing

Implementing An Outsourced Dual-Proxy Signing and Decryption...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Somchart Fugkeaw School of ICT Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand

Existing access control schemes that support fine-grained and secure data sharing for mobile cloud computing system typically focus on the design and development of secure and lightweight cryptographic protocols to enable data users efficiently decrypt the data by using mobile devices. However, some business requirements such as industrial cloud users may require the proof of authenticity of the data owner. Accordingly, both privacy-preserving and strong authenticity that data users can verify the authenticity of data owners to make trust the shared data are essential. To this end, we proposed a secure and efficient access control featured with efficient revocation in mobile cloud computing environment. Our proposed scheme is based on the outsourced dual proxy model where the expensive digital signing, encryption and decryption are partially offloaded to be executed by the delegated proxies in the cloud. We employed the symmetric encryption and the ciphertext policy attribute-based encryption (CP-ABE) as a core encryption scheme. In addition, we introduced a user revocation algorithm to enable the efficient user revocation in the mobile cloud computing. For the evaluation, we run the experiments to measure the performance of our core access control algorithms and the results demonstrated that our proposed scheme is efficient.

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