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 rea...
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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.
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 s...
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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.
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 re...
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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.
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 ri...
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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.
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. I...
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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.
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 the...
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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.
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, Artifici...
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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 computat...
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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.
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 programm...
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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.
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 han...
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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.
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