References of "Šmelko, Adam"
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See detailAstute Approach to Handling Memory Layouts of Regular Data Structures
Šmelko, Adam; Kruliš, Martin; Kratochvil, Miroslav UL et al

in Algorithms and Architectures for Parallel Processing (2023, January)

Programmers of high-performance applications face many challenging aspects of contemporary hardware architectures. One of the critical aspects is the efficiency of memory operations which is affected not ... [more ▼]

Programmers of high-performance applications face many challenging aspects of contemporary hardware architectures. One of the critical aspects is the efficiency of memory operations which is affected not only by the hardware parameters such as memory throughput or cache latency but also by the data-access patterns, which may influence the utilization of the hardware, such as re-usability of the cached data or coalesced data transactions. Therefore, a performance of an algorithm can be highly impacted by the layout of its data structures or the order of data processing which may translate into a more or less optimal sequence of memory operations. These effects are even more pronounced on highly-parallel platforms, such as GPUs, which often employ specific execution models (lock-step) or memory models (shared memory). In this work, we propose a modern, astute approach for managing and implementing memory layouts with first-class structures that is very efficient and straightforward. This approach was implemented in Noarr, a GPU-ready portable C++ library that utilizes generic programming, functional design, and compile-time computations to allow the programmer to specify and compose data structure layouts declaratively while minimizing the indexing and coding overhead. We describe the main principles on code examples and present a performance evaluation that verifies our claims regarding its efficiency. [less ▲]

Detailed reference viewed: 23 (1 UL)
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See detailGPU-Accelerated Mahalanobis-Average Hierarchical Clustering Analysis
Šmelko, Adam; Kratochvil, Miroslav UL; Kruliš, Martin et al

in Lecture Notes in Computer Science (2021, August), 12820

Hierarchical clustering is a common tool for simplification, exploration, and analysis of datasets in many areas of research. For data originating in flow cytometry, a specific variant of agglomerative ... [more ▼]

Hierarchical clustering is a common tool for simplification, exploration, and analysis of datasets in many areas of research. For data originating in flow cytometry, a specific variant of agglomerative clustering based Mahalanobis-average linkage has been shown to produce results better than the common linkages. However, the high complexity of computing the distance limits the applicability of the algorithm to datasets obtained from current equipment. We propose an optimized, GPU-accelerated open-source implementation of the Mahalanobis-average hierarchical clustering that improves the algorithm performance by over two orders of magnitude, thus allowing it to scale to the large datasets. We provide a detailed analysis of the optimizations and collected experimental results that are also portable to other hierarchical clustering algorithms; and demonstrate the use on realistic high-dimensional datasets. [less ▲]

Detailed reference viewed: 56 (1 UL)