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Multi-layer Access Control Mechanism based on Blockchain for Mobile Edge computing 18

Multi-layer Access Control Mechanism based on Blockchain for...

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18th IEEE Int Symp on parallel and distributed Proc with Applicat (ISPA) / 10th IEEE Int Conf on Big Data and Cloud Comp (BDCloud) / IEEE Int Symp on Social Comp and Networking (SocialCom) / IEEE Int Conf on Sustainable Comp and Commun (SustainCom)

作者： Hou, YiChen Liu, WenXin Lin, Hui Wang, XiaoDing Fujian Normal Univ Sch Math & Informat Fuzhou Peoples R China

ISBN: (纸本)9781665414852

Massive data generated by mobile terminals in edge computing brings new data security threats to data access control. However, previous access control strategies have deficiencies, i.e., coarse-graininess and poor flexibility, which cannot meet the requirements of secure data access to support various mobile edge computing applications. To overcome these shortcomings, in this paper, a Blockchain based Multi-layer Access Control mechanism, named BMAC, is proposed. Specifically, each resource data is assigned a security level. Then, an InfoMap based algorithm is developed to achieve credibility based users grouping. Next, a multi-blockchain is designed, based on which a flexible and fine-grained trusted data access control mechanism is established. The experiment results show that the BMAC scheme has better performance in terms of processing efficiency, throughput, CPU utilization, and latency.

关键词： Mobile edge computing Access control Blockchain

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GraphBLAS: C++ Iterators for Sparse Matrices

GraphBLAS: C++ Iterators for Sparse Matrices

引用

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

作者： Benjamin Brock Scott McMillan Aydı n Buluç Timothy G. Mattson José E. Moreira EECS Department University of California Berkeley CA Software Engineering Institute Carnegie Mellon University Pittsburgh PA Computational Research Department Lawrence Berkeley National Laboratory Berkeley CA Parallel Computing Lab Intel Ocean Park WA IBM Thomas J. Watson Research Center Yorktown Heights NY

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Iteration over opaque, generic data structures is an important feature of many C++ libraries. Aggressive compiler optimization and inlining enables generic C++ iterators to iterate over complex data structures with performance comparable to that of hand-tuned code with C-language (raw) pointers. In this paper, we describe the sparse matrix iterators in the current draft of the C++ GraphBLAS API, their support for a variety of backend data formats, and implementation strategies we have considered. We compare performance of these iterators to that of hand-tuned iteration with raw pointers, showing that our iterators introduce minimal overhead. We consider extensions to our iterator design for interoperability with the draft C++ Graph Library proposal and to support different semantics for iterating over sparse matrices (by row, by column, by specific diagonals, etc.).

关键词： distributed processing Conferences Semantics C++ languages Data structures Libraries Sparse matrices

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mpisee: MPI Profiling for Communication and Communicator Structure

mpisee: MPI Profiling for Communication and Communicator Str...

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

作者： Ioannis Vardas Sascha Hunold Jordy I. Ajanohoun Jesper Larsson Trä ff Faculty of Informatics Research Group Parallel Computing TU Wien Vienna Austria

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Cumulative performance profiling is a fast and lightweight method for gaining summary information about where and how communication time in parallel MPI applications is spent. MPI provides mechanisms for implementing such profilers that can be transparently used with applications. Existing profilers typically profile on a process basis and record the frequency, total time, and volume of MPI operations per process. This can lead to grossly misleading cumulative information for applications that make use of MPI features for partitioning the processes into different communicators. We present a novel MPI profiler, mpisee, for communicator-centric profiling that separates and records collective and point-to-point communication information per communicator in the application. We discuss the implementation of mpisee which makes significant use of the MPI attribute mechanism. We evaluate our tool by measuring its overhead and profiling a number of standard applications. Our measurements with thirteen MPI applications show that the overhead of mpisee is less than 3%. Moreover, using mpisee, we investigate in detail two particular MPI applications, SPLATT and GROMACS, to obtain information on the various MPI operations for the different communicators of these applications. Such information is not available by other, state-of-the-art profilers. We use the communicator-centric information to improve the performance of SPLATT resulting in a significant runtime decrease when run with 1024 processes.

关键词： Time-frequency analysis distributed processing Runtime Scalability Conferences Benchmark testing Topology

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SPHYNX: Spectral Partitioning for HYbrid aNd aXelerator-enabled systems 34

SPHYNX: Spectral Partitioning for HYbrid aNd aXelerator-enab...

引用

34th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Acer, Seher Boman, Erik G. Rajamanickam, Sivasankaran Sandia Natl Labs Ctr Comp Res POB 5800 Albuquerque NM 87185 USA

ISBN: (纸本)9781728174457

Graph partitioning has been an important tool to partition the work among several processors to minimize the communication cost and balance the workload. While accelerator-based supercomputers are emerging to be the standard, the use of graph partitioning becomes even more important as applications are rapidly moving to these architectures. However, there is no scalable, distributed-memory, multi-GPU graph partitioner available for applications. We developed a spectral graph partitioner, Sphynx, using the portable, accelerator-friendly stack of the Trilinos framework. We use Sphnyx to systematically evaluate the various algorithmic choices in spectral partitioning with a focus on GPU performance. We perform those evaluations on irregular graphs, because state-of-the-art partitioners have the most difficulty on them. We demonstrate that Sphynx is up to 17x faster on GPUs compared to the case on CPUs, and up to 580x faster compared to a state-of-the-art multilevel partitioner. Sphynx provides a robust alternative for applications looking for a GPU-based partitioner.

关键词： graph partitioning spectral partitioning GPUs distributed-memory systems

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Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation

Distributing Deep Learning Hyperparameter Tuning for 3D Medi...

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

作者： Josep Ll. Berral Oriol Aranda Juan Luis Dominguez Jordi Torres Barcelona Supercomputing Center Universitat Polit&#x00E9 cnica de Catalunya

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Most research on novel techniques for 3D Medical Image Segmentation (MIS) is currently done using Deep Learning with GPU accelerators. The principal challenge of such technique is that a single input can easily cope computing resources, and require prohibitive amounts of time to be processed. Distribution of deep learning and scalability over computing devices is an actual need for progressing on such research field. Conventional distribution of neural networks consist in “data parallelism”, where data is scattered over resources (e.g., GPUs) to parallelize the training of the model. However, “experiment parallelism” is also an option, where different training processes (i.e., on a hyper-parameter search) are parallelized across resources. While the first option is much more common on 3D image segmentation, the second provides a pipeline design with less dependence among parallelized processes, allowing overhead reduction and more potential scalability. In this work we present a design for distributed deep learning training pipelines, focusing on multi-node and multi-GPU environments, where the two different distribution approaches are deployed and benchmarked. We take as proof of concept the 3D U-Net architecture, using the MSD Brain Tumor Segmentation dataset, a state-of-art problem in medical image segmentation with high computing and space requirements. Using the BSC MareNostrum supercomputer as benchmarking environment, we use TensorFlow and Ray as neural network training and experiment distribution platforms. We evaluate the experiment speed-up when parallelizing, showing the potential for scaling out on GPUs and nodes. Also comparing the different parallelism techniques, showing how experiment distribution leverages better such resources through scaling, e.g. by a speed-up factor from x12 to x14 using 32 GPUs. Finally, we provide the implementation of the design open to the community, and the non-trivial steps and methodology for adapting and deploying a MIS

关键词： Training Deep learning Image segmentation Three-dimensional displays Scalability Pipelines parallel processing

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Improving Scalability with GPU-Aware Asynchronous Tasks

Improving Scalability with GPU-Aware Asynchronous Tasks

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

作者： Jaemin Choi David F. Richards Laxmikant V. Kale Department of Computer Science University of Illinois at Urbana-Champaign Urbana Illinois USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore California USA

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Asynchronous tasks, when created with over-decomposition, enable automatic computation-communication overlap which can substantially improve performance and scal-ability. This is not only applicable to traditional CPU-based systems, but also to modern GPU -accelerated platforms. While the ability to hide communication behind computation can be highly effective in weak scaling scenarios, performance begins to suffer with smaller problem sizes or in strong scaling due to fine-grained overheads and reduced room for overlap. In this work, we integrate G PU -aware communication into asynchronous tasks in addition to computation-communication overlap, with the goal of reducing time spent in communication and further increasing GPU utilization. We demonstrate the performance impact of our approach using a proxy application that performs the Jacobi iterative method, Jacobi3D. In addition to optimizations to minimize synchronizations between the host and GPU devices and increase the concurrency of GPU operations, we explore techniques such as kernel fusion and CUDA Graphs to mitigate fine-grained overheads at scale.

关键词： Jacobian matrices Performance evaluation Concurrent computing Scalability Graphics processing units Supercomputers Iterative methods

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SSB-Tree: Making Persistent Memory B+- Trees Crash-Consistent and Concurrent by Lazy-Box

SSB-Tree: Making Persistent Memory B+- Trees Crash-Consisten...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Tongliang Li Haixia Wang Airan Shao Dongsheng Wang Department of Computer Science Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China

The 8-byte granularity of failure-atomicity brings two challenges to Persistent Memory (PM) B+-tree designs. The first is how to insert a key in a sorted node atomically. The second is how to atomically perform structural modification operations that involve multiple nodes, such as node splits and merges. The majority of current designs either have huge consistency cost or compromise recovery time. In this paper, we propose an in-node logging technology, named Lazy-Box, to update multiple parts of a node without exposing intermediate states. Lazy-Box aggregates successive modifications and selectively uses Copy-on-Write (CoW) to reduce consistency cost. Based on Lazy-Box, we propose a new variant of B+-tree on PM, named Side-to-Side B+-Tree (SSB- Tree). SSB- Tree relaxes the tree-structure requirement so that node splits and merges only modify one node. Taking advantage of Lazy-Box, all modification operations of SSB-Tree are committed through a single 8-byte write. Therefore, SSB- Tree not only enables efficient concurrency protocol but also achieves instant recovery. Last but most important, SSB- Tree doubles the node space and reuses the extra space to avoid expensive node allocations when performing CoW. Our experimental results show that SSB-Tree achieves up to 29%, 40%, and 14% higher throughput in Insert, Delete, and Scan benchmark respectively than other state-of-the-art PM B+-trees.

关键词： Concurrent computing distributed processing Protocols Costs Cows Throughput Computer crashes

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Temporal Data Analytics on COVID-19 Data with Ubiquitous computing 18

Temporal Data Analytics on COVID-19 Data with Ubiquitous Com...

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作者： Chen, Yubo Leung, Carson K. Shang, Siyuan Wen, Qi Univ Manitoba Dept Comp Sci Winnipeg MB Canada

ISBN: (纸本)9781665414852

With technological advancements in computing and communications, huge amounts of big data are generated and collected at a very rapid rate from a wide variety of rich data sources. Embedded in these big data are useful information and valuable knowledge. An example is healthcare and epidemiological data such as data related to patients who suffered from viral diseases like the coronavirus disease 2019 (COVID-19). Knowledge discovered from these epidemiological data via data science helps researchers, epidemiologists and policy makers to get a better understanding of the disease, which may inspire them to come up ways to detect, control and combat the disease. In this paper, we present a temporal data science algorithm for analyzing big COVID-19 epidemiological data, with focus on the temporal data analytics with ubiquitous computing. The algorithm helps users to get a better understanding of information about the confirmed cases of COVID-19. Evaluation results show the benefits of our system in temporal data analytics of big COVID-19 data with ubiquitous computing. Although the algorithm is designed for temporal data analytics of big epidemiological data, it would be applicable to other temporal data analytics of big data in many real-life applications and services.

关键词： data science coronavirus disease COVID-19 big data temporal data data mining ubiquitous computing

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Memory Access Granularity Aware Lossless Compression for GPUs

Memory Access Granularity Aware Lossless Compression for GPU...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Sohan Lal Manuel Renz Julian Hartmer Ben Juurlink Technische Universit&#x00E4 t Hamburg Germany t Berlin Germany

High-bandwidth off-chip memory has played a key role in the success of Graphics Processing Units (GPUs) as an accelerator. However, as memory bandwidth scaling continues to lag behind the computational power, it remains a key bottleneck in computing systems. While memory compression has shown immense potential to increase the effective memory bandwidth by compressed data transfers between on-chip and off-chip memory, the large memory access granularity (MAG) of off-chip memory limits compression techniques from achieving a high effective compression ratio. Unfortunately, state-of-the-art lossless memory compression techniques do not take the large MAG of off-chip memory into account. A recent study has used MAG-aware approximation to increase the effective compression ratio, however, not all applications can tolerate errors, which limits its applicability. We propose extensions and GPU-specific optimizations to adapt a lossless memory compression technique to a MAG size to increase the effective compression ratio and performance gain. Our technique is based on the well-known Base-Delta-Immediate (BDI) compression technique that compresses a memory block to a common base and multiple deltas. We leverage the key observation that deltas often contain enough leading zeros to compress a block to a multiple of MAG without any loss of information. We show that MAG-aware BDI provides, on average, 48 % higher effective compression ratio, 10% (up to 27%) higher speedup, and 16% bandwidth reduction compared to normal BDI. While BDI, FPC, and CPACK have a similar compression ratio, MAG-aware BDI outperforms FPC, CPACK, and SLC by 56%, 47%, and 33%, respectively.

关键词： distributed processing Graphics processing units Bandwidth Performance gain Data transfer Encoding System-on-chip

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As easy as ABC: Optimal (A)ccountable (B)yzantine (C)onsensus is easy!

As easy as ABC: Optimal (A)ccountable (B)yzantine (C)onsensu...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Pierre Civit Seth Gilbert Vincent Gramoli Rachid Guerraoui Jovan Komatovic CNRS Sorbonne University NUS Singapore EPFL University of Sydney &#x00C9 cole Polytechnique Federale de Lausanne (EPFL)

It is known that the agreement property of the Byzantine consensus problem among $n$ processes can be violated in a non-synchronous system if the number of faulty processes exceeds $t_distributed$ = ┌ $n$ /3┐ − 1 [10], [19]. In this paper, we investigate the accountable Byzantine consensus problem in non-synchronous systems: the problem of solving Byzantine consensus whenever possible (e.g., when the number of faulty processes does not exceed $t_distributed$ ) and allowing correct processes to obtain proof of culpability of (at least) $t_distributed+ 1$ faulty processes whenever correct processes disagree. We present four complementary contributions: 1) We introduce ABC: a simple yet efficient transformation of any Byzantine consensus protocol to an accountable one. ABC introduces an overhead of only two all-to-all communication rounds and $O(n^distributed)$ additional bits in executions with up to $t_distributed$ faults (i.e., in the common case). 2) We define the accountability complexity, a complex-ity metric representing the number of accountability-specific messages that correct processes must send. Fur-thermore, we prove a tight lower bound. In particular, we show that any accountable Byzantine consensus protocol incurs cubic accountability complexity. Moreover, we illustrate that the bound is tight by applying the ABC transformation to any Byzantine consensus protocol. 3) We demonstrate that, when applied to an optimal Byzan-tine consensus protocol, ABC constructs an accountable Byzantine consensus protocol that is (1) optimal with respect to the communication complexity in solving consensus whenever consensus is solvable, and (2) op-timal with respect to the accountability complexity in obtaining accountability whenever disagreement occurs. 4) We generalize ABC to other distributed computing prob-lems besides the classic consensus problem. We charac-terize a class of agreement tasks, including reliable and consistent broadcast [5], that ABC renders accountable.

关键词： Measurement distributed processing Lattices Complexity theory Consensus protocol Reliability Task analysis

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