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Performant almost-latch-free data structures using epoch protection in more depth

VLDB JOURNAL 2024年第6期33卷 1793-1812页

作者： Li, Tianyu Chandramouli, Badrish Madden, Samuel MIT CSAIL Cambridge MA 02139 USA Microsoft Res Redmond WA USA

multi-core scalability presents a major implementation challenge for data system designers today. Traditional methods such as latching no longer scale in today's highly parallel architectures. While the designer can make use of techniques such as latch-free programming to painstakingly design specialized, highly-performant solutions, such solutions are often intricate to build and difficult to reason about. Of particular interest to data system designers is a class of data structures we call almost-latch-free;such data structures can be made scalable in the common case, but have rare complications (e.g., dynamic resizing) that prevent full latch-free implementations. In this work, we present a new programming framework called Epoch-Protected Version Scheme (EPVS) to make it easy to build such data structures. EPVS makes use of epoch protection to preserve performance in the common case of latch-free operations, while allowing users to specify critical sections that execute under mutual exclusion for the rare, non-latch-free operations. We showcase the use of EPVS-based concurrency primitives in a few practical systems to demonstrate its competitive performance and intuitive guarantees. EPVS is available in open source as part of Microsoft's FASTER project (Epoch Protected Version Scheme (source code) 2022;Microsoft FASTER 2022).

关键词： multi-core programming Concurrency control Epoch protection

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Parallel CRC optimisations on the x64 architecture: a per-partes method

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INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED SYSTEMS 2024年第3期39卷 292-316页

作者： Kaspar, Jiri Simecek, Ivan Czech Tech Univ Fac Informat Technol Prague Czech Republic Czech Tech Univ Fac Informat Technol Thakurova 9 Prague Czech Republic

Each generation of CPU provides more resources and new features. These increase the ability to perform algorithms faster and with a higher degree of parallelism. The article discusses methods used to optimise CRC generation algorithms for long data blocks with consideration of the capabilities of contemporary systems. We analysed known software CRC algorithms and combined all known principles into a solution scalable in multiple CPU cores on single and multi-socket systems. Various algorithms were evaluated on contemporary multicore systems with 1 x 4, 1 x 64, 2 x 12, and 4 x 26 cores. The results show how the performance is affected by the architecture of the memory subsystem. Compared to the original sequential Sarwate algorithm, our algorithms are 48.0, 51.1, 38.0, and 28.8 times faster. [GRAPHICS] .

关键词： CRC table-driven CRC calculation parallel CRC calculation multi-core programming multithreading

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Performance of Map-Reduce Using Java-8 Parallel Streams

Performance of Map-Reduce Using Java-8 Parallel Streams

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Computing Conference

作者： Lester, Bruce P. Maharishi Univ Management Comp Sci Dept Fairfield IA 52557 USA

ISBN: (纸本)9783030011741;9783030011734

The primary purpose of parallel streams in the recent release of Java 8 is to help Java programs make better use of multi-core processors for improved performance. However, in some cases, parallel streams can actually perform considerably worse than ordinary sequential Java code. This paper presents a Map-Reduce parallel programming pattern for Java parallel streams that produces good speedup over sequential code. An important component of the Map-Reduce pattern is two optimizations: grouping and locality. Three parallel application programs are used to illustrate the Map-Reduce pattern and its optimizations: Histogram of an Image, Document Keyword Search, and Solution to a Differential Equation. A proposal is included for a new terminal stream operation for the Java language called MapReduce() that applies this pattern and its optimizations automatically.

关键词： Parallel programming multi-core programming MapReduce Java parallel streams Parallel computing

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The Missing Link! A New Skeleton for Evolutionary multi-agent Systems in Erlang

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INTERNATIONAL JOURNAL OF PARALLEL programming 2018年第1期46卷 4-22页

作者： Stypka, Jan Turek, Wojciech Byrski, Aleksander Kisiel-Dorohinicki, Marek Barwell, Adam D. Brown, Christopher Hammond, Kevin Janjic, Vladimir AGH Univ Sci & Technol Al Mickiewicza 30 PL-30059 Krakow Poland Univ St Andrews Sch Comp Sci St Andrews Fife Scotland

Evolutionary multi-agent systems (EMAS) play a critical role in many artificial intelligence applications that are in use today. In this paper, we present a new generic skeleton in Erlang for parallel EMAS computations. The skeleton enables us to capture a wide variety of concrete evolutionary computations that can exploit the same underlying parallel implementation. We demonstrate the use of our skeleton on two different evolutionary computing applications: (1) computing the minimum of the Rastrigin function;and (2) solving an urban traffic optimisation problem. We show that we can obtain very good speedups (up to 142.44 the sequential performance) on a variety of different parallel hardware, while requiring very little parallelisation effort.

关键词： multi-core programming Erlang Agent-based computing Metaheuristics Many-core programming Algorithmic skeletons

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High throughput automatic muscle image segmentation using parallel framework

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BMC BIOINFORMATICS 2019年第1期20卷 158-158页

作者： Cui, Lei Feng, Jun Zhang, Zizhao Yang, Lin Northwest Univ Dept Informat Sci & Technol Xian Shaanxi Peoples R China Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL USA

Background: Fast and accurate automatic segmentation of skeletal muscle cell image is crucial for the diagnosis of muscle related diseases, which extremely reduces the labor-intensive manual annotation. Recently, several methods have been presented for automatic muscle cell segmentation. However, most methods exhibit high model complexity and time cost, and they are not adaptive to large-scale images such as whole-slide scanned specimens. Methods: In this paper, we propose a novel distributed computing approach, which adopts both data and model parallel, for fast muscle cell segmentation. With a master-worker parallelism manner, the image data in the master is distributed onto multiple workers based on the Spark cloud computing platform. On each worker node, we first detect cell contours using a structured random forest (SRF) contour detector with fast parallel prediction and generate region candidates using a superpixel technique. Next, we propose a novel hierarchical tree based region selection algorithm for cell segmentation based on the conditional random field (CRF) algorithm. We divide the region selection algorithm into multiple sub-problems, which can be further parallelized using multi-core programming. Results: We test the performance of the proposed method on a large-scale haematoxylin and eosin (H&E) stained skeletal muscle image dataset. Compared with the standalone implementation, the proposed method achieves more than 10 times speed improvement on very large-scale muscle images containing hundreds to thousands of cells. Meanwhile, our proposed method produces high-quality segmentation results compared with several state-of-the-art methods. Conclusions: This paper presents a parallel muscle image segmentation method with both data and model parallelism on multiple machines. The parallel strategy exhibits high compatibility to our muscle segmentation framework. The proposed method achieves high-throughput effective cell segmentation on large-scale muscl

关键词： Muscle image segmentation Cloud computing multi-core programming

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A multi-core computing approach for large-scale multi-label classification

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INTELLIGENT DATA ANALYSIS 2017年第2期21卷 329-352页

作者： Rodriguez, Juan Manuel Godoy, Daniela Mateos, Cristian Zunino, Alejandro Unicen Univ ISISTAN Res Inst Campus UnivTandil B7001BBO Tandil Buenos Aires Argentina

Large scale multi-label learning, i.e. the problem of determining the associated set of labels for an instance, is gaining relevance in recent years due to the emergence of several real-world applications. Most notably, the exponential growth of the SocialWeb where a resource can be labeled by millions of users using one or more tags, i.e. a resource can be associated to several labels at the same time. A well-known approach for multi-label classification is the Binary Relevance (BR) algorithm which trains a binary classifier for each label independently. However, the serial implementation of BR is not suitable for medium or large datasets due to the time and computational resources required for training. For example, training classifiers for mid-size datasets using MULAN implementation of BR might take several weeks. This paper discusses a parallel implementation of the MULAN BR technique that harnesses the computational power of nowadays multi-core processors. Our implementation presents a speed-up in the training phase of up to 12 times when compared to the original MULAN implementation. In addition, the cross-validation technique of MULAN had huge RAM requirements, making it unusable with large datasets. Therefore, we have overcome this limitation by using compact data structures and taking advantage of disk caching. We have also compared our implementation against scikit-learn, a popular tool for data mining and data analysis, showing significant improvements in speed-up.

关键词： Binary relevance classification multi-core programming parallel classification multi-label classification

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A TEACHING SCHEMA FOR multi-core programming

A TEACHING SCHEMA FOR MULTI-CORE PROGRAMMING

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2nd International Conference on Computer Supported Education

作者： Gao, Yun Zhang, Xuejie Yunnan Univ Ctr High Performance Comp 2 North Cuihu Rd Kunming Peoples R China Yunnan Univ Sch Informat Sci & Engn Kunming Peoples R China

ISBN: (纸本)9789896740245

As multi-core processors become more and more widespread, multi-core programming attracts attention of the entire society. On this background, the multi-core programming curricula have been opened to the senior undergraduate students in many universities. At present, the teaching schemas about multi-core programming are scarcely mentioned. In this paper, we present a teaching schema on the specific characteristics of multi-core programming curriculum, combining our teaching practice. The schema pays attention to arousing students' enthusiasm and positivity, showing instance demos and improving students' consciousnesses of optimizing multi-core programs. During our practical teaching, the schema has obtained a better teaching effect.

关键词： multi-core programming Teaching schema Positivity Instance

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Accelerating collision detection for large-scale crowd simulation on multi-core and many-core architectures

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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2014年第1期28卷 33-49页

作者： Vigueras, Guillermo Orduna, Juan M. Lozano, Miguel Cecilia, Jose M. Garcia, Jose M. Univ Valencia Dept Informat E-46100 Burjassot Valencia Spain Univ Valencia Dept Informat Networking & Virtual Environm Grp E-46100 Burjassot Valencia Spain Univ Murcia Dept Ingn & Tecnol Comp E-30001 Murcia Spain

The computing capabilities of current multi-core and many-core architectures have been used in crowd simulations for both enhancing crowd rendering and simulating continuum crowds. However, improving the scalability of crowd simulation systems by exploiting the inherent parallelism of these architectures is still an open issue. In this paper, we propose different parallelization strategies for the collision check procedure that takes place in agent-based simulations. These strategies are designed for exploiting the parallelism in both multi-core and many-core architectures like graphic processing units (GPUs). As for the many-core implementations, we analyse the bottlenecks of a previous GPU version of the collision check algorithm, proposing a new GPU version that removes the bottlenecks detected. In order to fairly compare the GPU with the multi-core implementations, we propose a parallel CPU version that uses read--copy update (RCU), a new synchronization method which significantly improves performance. We perform a comparison study of these different implementations. On the one hand, the comparison study shows the first performance evaluation of RCU in a real user-space application with complex data structures. On the other hand, the comparison shows that the GPU greatly accelerates the collision test with respect to any other implementation optimized for multi-core CPUs. In addition, we analyse the efficiency of the different implementations taking into account the theoretical performance and power consumption of each platform. The evaluation results show that the GPU-based implementation consumes less energy and provides a minimum speedup of 45x with respect to any of the CPU-based implementations. Since interactivity is a hard constraint in crowd simulations, this acceleration of the collision check process represents a significant improvement in the overall system throughput and response time. Therefore, the simulations are significantly accelerated, and the

关键词： multi-core programming GPU programming crowd simulations collision check procedure performance improvement

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Construction of Porous Networks subjected to Geometric Restrictions by using OpenMP 28

Construction of Porous Networks subjected to Geometric Restr...

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28th IEEE International Parallel & Distributed Processing Symposium Workshops (IPDPSW)

作者： Gonzalez-Mendez, A. Roman-Alonso, G. Rojas-Gonzalez, F. Castro-Garcia, M. A. Aguilar-Cornejo, M. Cordero-Sanchez, S. Univ Autonoma Metropolitana Izt Dept Elect Engn Iztapalapa CDMX Mexico Univ Autonoma Metropolitana Izt Dept Chem Iztapalapa CDMX Mexico

ISBN: (纸本)9781479941162

The study of porous materials involves great importance for a vast number of industrial applications. In order to study some specific characteristics of materials, in-silico simulations can be employed. The particular simulation of pore networks described in this work finds its basis in the Dual Site-Bond Model (DSBM). Under this approach, a porous material is thought to be made of sites (cavities, bulges) interconnected to each other through bonds (throats, capillaries);while every site is connected to a number of bonds each bond is the link between two sites. At present, several computing algorithms have been implemented for the simulation of pore networks;nevertheless, only a few of these methods take into account the geometric restrictions that arise during the interconnection of a set of bonds to every site of the network. It is likely that introducing restrictions of this sort in the computing algorithms would lead to the implementation of more realistic pore networks. In this work, a sequential algorithm and its parallel computing version are proposed to construct pore networks, allowing geometrical restrictions among hollow entities. Our parallel approach uses OpenMP to create a set of threads (computing tasks) that work simultaneously on independent and random pore network regions. We discuss the obtained results.

关键词： Porous Networks Parallel Computing Dual Site-Bond Model multi-core programming OpenMP

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A Reconfigurable MapReduce Accelerator for multi-core all-programmable SoCs 16

A Reconfigurable MapReduce Accelerator for multi-core all-pr...

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16th International Symposium on System-on-Chip (SoC)

作者： Kachris, Christoforos Sirakoulis, Georgios Ch. Soudris, Dimitrios Democritus Univ Thrace DUTH Dept Elect & Comp Engn GR-67100 Xanthi Greece Natl Tech Univ Athens Dept Elect & Comp Engn Athens Greece

ISBN: (纸本)9781479968909

Phoenix MapReduce is a programming framework for multi-core systems that is used to automatically parallelize and schedule the programs based on the MapReduce framework. This paper presents a novel reconfigurable MapReduce accelerator that can be augmented to multi-core SoCs and it can speedup the indexing and the processing of the MapReduce key-value pairs. The proposed architecture is implemented, mapped and evaluated to an all-programmable SoC with two embedded ARM cores (Zynq FPGA). Depending on the MapReduce application requirements, the user can dynamically reconfigure the FPGA with the appropriate version of the MapReduce accelerator. The performance evaluation shows that the proposed scheme can achieve up to 2.3x overall performance improvement in MapReduce applications.

关键词： multi-core programming MapReduce scratchpad memory

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