arXiv subjects

Temitayo Adefemi

Publications and source records attributed to Temitayo Adefemi.

6 recordsLinked to original sources

arXiv2025

Do MPI Derived Datatypes Actually Help? A Single-Node Cross-Implementation Study on Shared-Memory Communication

MPI's derived datatypes (DDTs) promise easier, copy-free communication of non-contiguous data, yet their practical performance remains debated and is often reported only for a single MPI stack. We present a cross-implementation assessment using three 2D applications: a Jacobi CFD solver, Conway's Game of Life, and a lattice-based image reconstruction. Each application is written in two ways: (i) a BASIC version with manual packing and unpacking of non-contiguous regions and (ii) a DDT version using MPI_Type_vector and MPI_Type_create_subarray with correct true extent via MPI_Type_create_resized. For API parity, we benchmark identical communication semantics: non-blocking point-to-point (Irecv/Isend + Waitall), neighborhood collectives (MPI_Neighbor_alltoallw), and MPI-4 persistent operations (*_init). We run strong and weak scaling on 1-4 ranks, validate bitwise-identical halos, and evaluate four widely used MPI implementations: MPICH, Open MPI, Intel MPI, and MVAPICH2 on a single ARCHER2 node. Results are mixed. DDTs can be fastest, for example for the image reconstruction code on Intel MPI and MPICH, but can also be among the slowest on other stacks, such as Open MPI and MVAPICH2 for the same code. For the CFD solver, BASIC variants generally outperform DDTs across semantics, whereas for Game of Life the ranking flips depending on the MPI library. We also observe stack-specific anomalies, for example MPICH slowdowns with DDT neighborhood and persistent modes. Overall, no strategy dominates across programs, semantics, and MPI stacks; performance portability for DDTs is not guaranteed. We therefore recommend profiling both DDT-based and manual-packing designs under the intended MPI implementation and communication mode. Our study is limited to a single node and does not analyze memory overhead; multi-node and GPU-aware paths are left for future work.

cs.DC↗

arXiv2025

The Entropy of Parallel Systems

Ever since Claude Shannon used entropy for his "Mathematical Theory of Communication", entropy has become a buzzword in research circles with scientists applying entropy to describe any phenomena that are reminiscent of disorder. In this paper, we used entropy to describe the incompatibility between components in the computer, which can cause noise and disorder within the parallel cluster. We develop a mathematical theory, primarily based on graph theory and logarithms, to quantify the entropy of a parallel cluster by accounting for the entropy of each system within the cluster. We proceed using this model to calculate the entropy of the Top 10 supercomputers in the Top500 list. Our entropy framework reveals a statistically significant negative correlation between system entropy and computational performance across the world's fastest supercomputers. Most notably, the LINPACK benchmark demonstrates a strong negative correlation (r = -0.7832, p = 0.0077) with our entropy measure, indicating that systems with lower entropy consistently achieve higher computational efficiency, this Relationship is further supported by moderate correlations with MLPerf mixed-precision benchmarks (r = -0.6234) and HPCC composite scores (r = -0.5890), suggesting the framework's applicability extends beyond traditional dense linear algebra workloads.

cs.DC↗

arXiv2025

What Every Computer Scientist Needs To Know About Parallelization

Parallelization has become a cornerstone of modern computing, influencing everything from high performance supercomputers to everyday mobile devices. This paper presents a comprehensive guide on the fundamentals of parallelization that every computer scientist should know, beginning with a historical perspective that traces the evolution from early theoretical models such as PRAM and BSP to today's advanced multicore and heterogeneous architectures. We explore essential theoretical frameworks, practical paradigms, and synchronization mechanisms while discussing implementation strategies using processes, threads, and modern models like the Actor framework. Additionally, we examine how hardware components including CPUs, caches, memory, and accelerators interact with software to impact performance, scalability, and load balancing. This work demystifies parallel programming by integrating historical context, theoretical underpinnings, and practical case studies. It equips readers with the tools to design, optimize, and troubleshoot parallel applications in an increasingly concurrent computing landscape.

cs.DC↗

arXiv2024

Analysis of the Performance of the Matrix Multiplication Algorithm on the Cirrus Supercomputer

Matrix multiplication is integral to various scientific and engineering disciplines, including machine learning, image processing, and gaming. With the increasing data volumes in areas like machine learning, the demand for efficient parallel processing of large matrices has grown significantly.This study explores the performance of both serial and parallel matrix multiplication on the Cirrus supercomputer at the University of Edinburgh. The results demonstrate the scalability and efficiency of these methods, providing insights for optimizing matrixmultiplication in real-world applications.

cs.DC↗

arXiv2024

Cellular Automata as a Network Topology

Cellular automata represent physical systems where both space and time are discrete, and the associated physical quantities assume a limited set of values. While previous research has applied cellular automata in modeling chemical, biological, and physical systems, its potential for modeling topological systems, specifically network topologies, remains underexplored. This paper investigates the use of cellular automata to model decentralized network topologies, which could enhance load balancing, fault tolerance, scalability, and the propagation and dissemination of information in distributed systems.

nlin.CG↗

arXiv2024

A Conceptual Framework For Trie-Augmented Neural Networks (TANNS)

Trie-Augmented Neural Networks (TANNs) combine trie structures with neural networks, forming a hierarchical design that enhances decision-making transparency and efficiency in machine learning. This paper investigates the use of TANNs for text and document classification, applying Recurrent Neural Networks (RNNs) and Feed forward Neural Networks (FNNs). We evaluated TANNs on the 20 NewsGroup and SMS Spam Collection datasets, comparing their performance with traditional RNN and FFN Networks with and without dropout regularization. The results show that TANNs achieve similar or slightly better performance in text classification. The primary advantage of TANNs is their structured decision-making process, which improves interpretability. We discuss implementation challenges and practical limitations. Future work will aim to refine the TANNs architecture for more complex classification tasks.

cs.CL↗