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IEEE International Conference on Smart Cloud (SmartCloud)

作者： Smriti Kumari Pooja Rani Pallavi Joshi Computer Science and Engineering Texas A&M University Commerce Texas Department of CTECH SRMIST SRM University KTR Chennai India

ISBN: (数字)9798331596651

ISBN: (纸本)9798331596668

Cluster Computing Systems (CCS) is a type of technology that not only causes computing power improvement but also utilizes energy to a lesser degree by taking advantage of parallel programming while processing and reading massive amounts of data. We can have multiple Central Processing Units (CPUs) and storage devices (disks) where the massive size of data can be processed. However, Cluster Computing System also comes with its own set of challenges such as if for a reason the node stops operating, nodes stops communicating with each other and the data transfer doesn’t happen due to poor network which can lead to bottleneck while processing massive amounts of data. To overcome these issues, a well reputed tech giant known as Google, came up with a solution known as MapReduce. MapReduce is a framework designed for Big Data which takes care of processing large amounts of data over various servers. In this paper, we outline how CCS works and the challenges it faces today in the age of massive data. The introduction to some well received measures of Big Data are presented by us in this paper. These solutions show us the way we can address the issues we face in CSS. The primary goal of this writing is to look into the issues that we might face and the most efficient ways to resolve it in CSS.

关键词： Surveys parallel programming File systems Scalability Cluster computing Big Data Internet Servers Faces Optimization

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Teaching Performance Metrics in parallel Computing Courses

Teaching Performance Metrics in Parallel Computing Courses

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IEEE International Symposium on parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Sandino Vargas-Pérez Department of Computer Science Kalamazoo College Kalamazoo MI USA

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

As high-performance computing technologies advance, the significance of parallel programming in various domains is becoming increasingly evident since it allows us to harness the power of heterogeneous computing and solve complex problems more efficiently. However, for students to master this type of computation and be able to apply it in different contexts, it requires understanding how measuring and optimizing parallel code impacts its performance. This paper presents an approach to enhancing students' comprehension of parallel performance metrics through an interactive exercise that complements lectures on parallel performance and improves assessment.

关键词： Measurement Distributed processing Data analysis parallel programming High performance computing Conferences Education

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HPC master design: experience from Pisa

HPC master design: experience from Pisa

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Euromicro Conference on parallel, Distributed and Network-Based Processing

作者： Marco Danelutto Dept. of Computer Science Univ. of Pisa

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

HPC is a widely used term, often referred to the applications, architectures and programming models and tools targeting highly parallel machines such as those of the *** lists. Recent advances in computing hardware resources require application of HPC techniques when using much smaller machines. Indeed, proper parallel programming tools and applications are needed also to exploit parallel hardware resources in personal computers (laptops, desktops, servers). This paper outlines key challenges in designing master’s degree programs in HPC and shares lessons learned from various experiences in developing and implementing such programs in Italy and Europe.

关键词： Portable computers parallel programming High performance computing Europe parallel machines Hardware Telecommunications Stakeholders Servers programming profession

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Utilizing the Kokkos Framework for Scalable Application parallelization

Utilizing the Kokkos Framework for Scalable Application Para...

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Telecommunications Forum (TELFOR)

作者： Aleksa Paunović Matija Dodović Marko Mišić School of Electrical Engineering University of Belgrade Belgrade Serbia

ISBN: (数字)9798350391060

ISBN: (纸本)9798350391077

New, efficient parallel paradigms and models have been one of the main focuses of the research community in the domain of high-performance computing and multiprocessing systems. It is especially emphasized with the rise of many-core processors, such as graphics processing units. The development of parallel applications for different multi-core and many-core architectures becomes even harder, as it is usually needed to maintain different codebases. In this paper, we evaluate Kokkos, a new high-level parallel programming model and framework aimed at developing portable parallel programs for different architectures. We evaluated it using five different benchmark applications on the CPU and compared the results with OpenMP counterparts. The results and experiences are discussed in the paper.

关键词： parallel programming Computational modeling High performance computing Graphics processing units Computer architecture Benchmark testing Libraries Central Processing Unit Telecommunications Multiprocessing systems

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Efficient parallel Algorithm for Detecting Longest Flow Paths in Flow Direction Grids

Efficient Parallel Algorithm for Detecting Longest Flow Path...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Karunamuni Jayarukshi Shivali Agarwal Girish K K Santosh Goudar Biswajit Bhowmik Dept. of Computer Science and Engineering Maharshi Sushrut CAS Lab BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat Dept. of Computer Science and Engineering Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat Dept. of Computer Science and Engineering BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

High-performance computing (HPC) has transformed the capacity to address complex computational tasks across various scientific fields by enabling the efficient processing of large datasets and intricate simulations. In hydrological modeling, a critical task is identifying the longest flow channel within a catchment, which is essential for understanding water flow patterns and managing resources. However, existing geographic information system (GIS) algorithms for flow path identification often suffer from inefficiencies and inaccuracies. To address these challenges, this paper introduces innovative parallel methods utilizing Open Multi-Processing (OpenMP), a widely-used API that supports multi-platform shared-memory parallel programming. This approach optimizes the analysis of flow direction data, resulting in faster and more accurate identification of flow channels. The results demonstrate that the proposed method outperforms current approaches, offering substantial improvements in both performance and precision. These advancements have the potential to significantly enhance hydrological modeling practices and water resource management.

关键词： Accuracy parallel programming Computational modeling Scalability Redundancy Computational efficiency Reliability parallel algorithms Water resources Geographic information systems

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Performance Impact of Removing Data Races from GPU Graph Analytics Programs

Performance Impact of Removing Data Races from GPU Graph Ana...

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IEEE International Workshop/Symposium on Workload Characterization

作者： Yiqian Liu Avery VanAusdal Martin Burtscher Department of Computer Science Texas State University San Marcos TX USA

ISBN: (数字)9798350356038

ISBN: (纸本)9798350356045

Some of the fastest CUDA codes contain "benign" data races to boost their performance. However, such races can lead to unpredictable behavior and incorrect results on other hardware and compilers, making their elimination crucial for producing reliable and portable programs. This paper investigates the performance impact of removing data races from six high-end graph analytics codes. We identify and eliminate the races from these GPU programs by adding necessary synchronization and validating their correctness. We present our race-free codes and their original versions as an open-source suite. Comparing the performance of our new codes with their baseline counterparts on multiple inputs and GPUs, we observe that race-free implementations do not always incur a performance penalty. In fact, some race-free versions are faster, with our validated maximal independent set implementation achieving a 5-11% speedup. Our findings indicate that race-free code can reach comparable or even superior performance, supporting the adoption of best practices for parallel programming.

关键词： Codes parallel programming Graphics processing units Hardware Synchronization Reliability Best practices

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Challenges in High-Performance Computing

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Journal of the Brazilian Computer Society 2023年第1期29卷 51-62页

作者： Navaux, Philippe Olivier Alexandre Lorenzon, Arthur Francisco Serpa, Matheus da Silva Federal University of Rio Grande do Sul Brazil

High-Performance Computing, HPC, has become one of the most active computer science fields. Driven mainly by the need for high processing capabilities required by algorithms from many areas, such as Big Data, Artificial Intelligence, Data Science, and subjects related to chemistry, physics, and biology, the state-of-art algorithms from these fields are notoriously demanding computer resources. Therefore, choosing the right computer system to optimize their performance is paramount. This article presents the main challenges of future supercomputer sys-tems, highlighting the areas that demand the most of HPC servers; the new architectures, including heterogeneous processors composed of artificial intelligence chips, quantum processors, the adoption of HPC on cloud servers; and the challenges of software developers when facing parallelizing applications. We also discuss challenges regarding non-functional requirements, such as energy consumption and resilience. © 2023, Brazilian Computing Society. All rights reserved.

关键词： parallel programming

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Taskgraph Framework: A Competitive Alternative to the OpenMP Thread Model

Taskgraph Framework: A Competitive Alternative to the OpenMP...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Snehal Chavan Prathamesh Nile Sunil Kumar Biswajit Bhowmik Dept. of Computer Science and Engineering Maharshi Sushrut CAS Lab BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat Dept. of Computer Science and Engineering Maharshi Patanjali CPS Lab BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat Dept. of Computer Science and Engineering BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

OpenMP is the predominant standard for shared memory systems in high-performance computing (HPC), offering a tasking paradigm for parallelism. However, existing OpenMP implementations, like GCC and LLVM, face computational limitations that hinder performance, especially for large-scale tasks. This paper presents the Taskgraph framework, a novel solution that overcomes the limitations of traditional task dependency graphs (TDGs). Unlike conventional TDGs, which require costly reconstruction for dynamic program structures, the Taskgraph framework uses a taskgraph clause with a list of variables, enabling real-time adaptation without complete reconstruction. This approach significantly reduces overhead, making the Task-graph model highly efficient for tasks with minimal dependencies, offering a competitive alternative to the OpenMP thread model, and enhancing efficiency and adaptability in dynamic HPC environments.

关键词： Adaptation models Runtime parallel programming High performance computing Memory management parallel processing Real-time systems Intelligent systems Standards Faces

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Machine Learning-Driven Web System for Automated Sequential-to-parallel Code Transformation

Machine Learning-Driven Web System for Automated Sequential-...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： I. V. Viktorov R. F. Gibadullin Department of Computer Systems Kazan National Research Technical University named after A. N. Tupolev – KAI Kazan Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

This paper presents the design and implementation of a machine learning-driven web system for automating the transformation of sequential code into parallel code. The proposed system leverages a shared memory programming model and is built on a microservices architecture to ensure scalability and modularity. It incorporates advanced tools for constructing and analyzing Abstract Syntax Trees to generate parallel programs that adhere to Bernstein's conditions. A supervised learning mechanism, using Word2Vec for vector representation and TF-IDF for significance evaluation, enables the system to adapt and refine parallelization rules based on programmer input. Additionally, the system integrates an AMQP-based message broker to handle high-load processing efficiently and supports data storage through relational or NoSQL databases. The solution provides a robust and flexible platform for enhancing computational performance and reducing development complexity in parallel programming. The results demonstrate the feasibility and effectiveness of this approach in addressing the challenges of automating code parallelization in modern software systems.

关键词： Codes parallel programming Scalability NoSQL databases Supervised learning Microservice architectures Machine learning Syntactics Software systems Vectors

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Speedcode: Software Performance Engineering Education via the Coding of Didactic Exercises

Speedcode: Software Performance Engineering Education via th...

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IEEE International Symposium on parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Tim Kaler Xuhao Chen Brian Wheatman Dorothy Curtis Bruce Hoppe Tao B. Schardl Charles E. Leiserson MIT CSAIL Johns Hopkins University Connective Associates

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

This paper introduces Speedcode, an online programming platform that aims to improve the accessibility of software performance-engineering education. At its core, Speedcode provides a platform that lets users gain hands-on experience in software performance engineering and parallel programming by completing short programming exercises. Speedcode challenges users to develop fast multicore solutions for short programming problems and evaluates their code's performance and scalability in a quiesced cloud environment. Speedcode supports parallel programming using OpenCilk, a task-parallel computing platform that is open-source and easy to program, teach and use for research. Speedcode aims to reduce barriers to learning and teaching software performance engineering. It allows users to run and evaluate their code on modern multicore machines from their own computer without installing any software. This provides users an easy introduction to the topic, and enables teachers to more easily incorporate lessons on software performance engineering into their courses without incurring the onerous overhead of needing to setup computing environments for their students.

关键词： Distributed processing Multicore processing parallel programming Scalability Prototypes Software performance Encoding

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