OBPMark and OBPMark-ML are computational benchmarks developed by ESA and BSC/UPC for on-board data processing and machine learning in space applications. These benchmarks aim to standardize performance comparison across different processing devices, identify key parameters, and provide recommendatio

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Delve into the world of supercomputing with a crash course covering parallelism, MPI, OpenMP, and hybrid programming. Learn about dividing tasks for efficient execution, exploring parallelization strategies, and the benefits of working smarter, not harder. Discover how everyday activities, such as p

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Material preparation plays a crucial role in the combing process in textile manufacturing. It involves feeding combers with a small lap, ensuring fibers are evenly arranged and parallelized for efficient combing operation. Lack of parallelization can lead to fiber loss and poor quality output. Prope

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Play a critical role in deciding the combing performance. Factors include short fibre content, fibre stiffness, moisture content, fiber fineness, and foreign material. Material preparation such as fiber parallelization, sheet thickness, and evenness is crucial. Machine conditions, speeds, operation,

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MapReduce is a powerful model for processing massive amounts of data in parallel through distributed systems like Apache Hadoop. This technology, popularized by Google, enables automatic parallelization and fault tolerance, allowing for efficient data processing at scale. Learn about the motivation

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Our project focuses on enhancing the efficiency of 2D convolutions by implementing parallelization with GPUs. We delve into the significance of convolutions, strategies for parallelization, challenges faced, and the outcomes achieved. Through comparing direct convolution to Fast Fourier Transform (F

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MapReduce is a system designed for distributed processing of large datasets, providing automatic parallelization, fault tolerance, and clean abstraction for programmers. It allows for easy writing of distributed programs with built-in reliability on large clusters. Despite its popularity in the late

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This presentation delves into DDS Prediction, a technique designed to reduce extreme tail latency in web search engines by optimizing query execution times and parallelizing specific queries. It addresses the challenges of improving latency for all users and emphasizes the importance of achieving hi

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Explore the challenges of race conditions in C++ multithreading, from basic demonstrations to advanced scenarios. Delve into C++11 features like atomic operations, memory ordering, and synchronization primitives to create efficient and thread-safe applications.

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Explore how merge semantics in STMs enable speculative parallelization, addressing irregular parallelism challenges. The research covers connected components, parallel applications, and the utilization of speculation for optimized execution in diverse computing scenarios.

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Exploring the concepts of parallel sorting algorithms, analyzing parallel programs, divide and conquer algorithms, parallel speed-up, estimating running time on multiple processors, and understanding Amdahl's Law in parallel computing. The content covers key measures of run-time, divide and conquer

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GPUs are widely used for high-performance computing, but irregular algorithms pose challenges for parallelization. This study delves into the microarchitectural aspects affecting GPU performance, emphasizing best practices to optimize irregular GPU kernels. The impact of branch divergence, memory co

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Studying data dependencies in nested loops is crucial for optimizing code performance. The analysis involves assessing dependencies across loop iterations, iteration numbers, iteration vectors, and loop nests. Dependencies in loop nests are determined by iteration vectors, memory accesses, and write

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Explore the challenges of race conditions in parallel programming, learn how to handle shared states in separate threads, and discover advanced synchronization methods in C++. Delve into features from C++11 to C++20, including atomic operations, synchronization primitives, and coordination types. Un

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Domain decomposition is a parallelization method used for developing MPI programs by partitioning the domain into portions and assigning them to different processes. Three common ways of partitioning are block, cyclic, and block-cyclic, each with its own communication requirements. Considerations fo

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This collection of reports details the technical tasks undertaken in 2022 by AMU in managing interfaces with ACH for Eiron and IMAS, as well as contributions to EIRENE_unified. It also discusses the parallelization of rate coefficient calculations and the implementation of MPI parallelisation for ef

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Shared-memory computing with Open MP offers a parallel programming model that is portable and scalable across shared-memory architectures. It allows for incremental parallelization, compiler-based thread program generation, and synchronization. Open MP pragmas help in parallelizing individual comput

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Dive into the world of real-time ray tracing with part 2 of this series, focusing on parallelizing your ray tracer for optimal performance. Explore the essentials needed before GPU ray tracing, handle materials, textures, and mesh files efficiently, and understand the complexities of rendering trian

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The document delves into the realm of multi-threaded active objects, exploring their principles, limitations, related works, and solutions. It covers topics such as asynchronous method calls, first-class futures, and the risks associated with active objects. Additionally, it compares various approac

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