Proceedings of the Brazilian Symposium on High Performance Computing Systems (SSCAD)
type: channel
description: O Simpósio de Sistemas Computacionais de Alto Desempenho (WSCAD) é um evento anual que apresenta, desde o ano 2000, os principais desenvolvimentos, aplicações e tendências nas áreas de Arquitetura de Computadores, Processamento de Alto Desempenho e Sistemas Distribuídos.
publisher: Sociedade Brasileira de Computação
language: pt-BR
publication name: Proceedings of the Brazilian Symposium on High Performance Computing Systems (SSCAD)
issn: 0000-0000
items: Revisiting Concurrency Classics: Implementation and Evaluation in OpenMP, Rust, and Go
items: Dependability Analysis of Weather Monitoring Systems Considering Different Redundancy Mechanisms
items: Assessing the Performance and Impact of an Open-Source RTI in Distributed Real-Time Military Simulation
items: Fault Tolerance Implementation in the Lattice Boltzmann Method for Resilient Execution on AWS Ephemeral Instances
items: Predicting FaaS Runtime with the Orama Framework Using Machine Learning
items: Performance Evaluation of N-Body Simulations on AWS with StarPU, OpenMP and MPI Runtime Systems
items: Buffer Pool Parameter Optimization with Machine Learning in Non-Transactional Environments
items: An Experimental Study of Variable Neighborhood Search for General-Purpose Subgraph Optimization in Parallel Systems
items: Preventing Out-Of-Memory Errors in Dask through Automated Memory-Aware Chunking
items: Estimating CO2 emissions of distributed applications and platforms with SimGrid_Batsim
items: Web Simulator for Teaching Computer Architecture with Vector and Cache Support
items: Interface for Programming Fault-Tolerant Linear Pipelines for Standard C++ MPI
items: Parallelization of Constraint List Generation in Multiple Genetic Sequence Alignments
items: Investigating Contention Management in a Distributed Transactional Memory System
items: Evaluating Machine Learning Algorithms for Anomaly Detection in Industrial Engines on Edge Devices
items: Large-Scale RISC-V Processor Verification Using Automated Design Inspection and a Generic Simulation Method
items: Evaluating Memory Constraints of RISC-V Matrix Accelerators using gem6
items: Integration and performance analysis of parallel RISC-V architectures
items: Quantum Machine Learning with Enhanced Autoencoders for Intrusion Detection
items: Implementing Cold-Start Reduction Techniques on Globus Compute
items: Adaptive Detection of Software Aging under Workload Shift
items: MSE: A Matrix Sparsity Extension for RISC-V
items: NUMA-Aware Task Scheduling Strategy Aiming to Reduce Cache Conflicts
items: Can OpenMP Scale Beyond the Node? A Performance Evaluation of Remote Offloading via the MPI Proxy Plugin
items: Implementation and Evaluation of Warp Scheduling Policies in Vortex, an Open-Source GPGPU
items: A Literature Review on Live Migration in Container-Based Distributed Systems
items: Optimization of Sustainable AI Factories through GPU Sharing
items: Data Compression for I/O Reduction in High-Performance Seismic Simulations
items: Workflow for Exact Sequence Alignment in High-Performance Computing Systems
items: A Weighted Bi-objective Strategy for Executing Scientific Workflows in Containerized Environments
items: Federated Outlier Detection for Astronomical Data: Performance Analysis on Commercial Clouds
items: Fast-Tracking Scalability Analysis: The PaScal Paramount Approach
items: Analyzing Energy Management Techniques in GPU-Accelerated Applications in an Exascale System
items: HPC on a budget: on the CPU's impact on dense linear algebra computation with GPUs
items: Optimizing Graph Structures in Persistent Memory for NUMA Architectures
items: A Client-Side Architecture for Enhancing Productivity of Interactive Parallel Scalability Analysis with PaScal Suite
items: Efficient MFCC Extraction on FPGA: An Open and Flexible Implementation
items: Intrusiveness and Scalability of OMPT-Based Tracing Tools for Task-based OpenMP Applications
items: Predictive Modeling of Energy-Delay Product for Submission Optimization in Supercomputers
items: To Pin or not to Pin: That is the question
items: PowerTrackZ: Energy efficiency analysis of access points in wireless networks
items: Overcoming Limits in GPU Multi-Partitioning

Revisiting Concurrency Classics: Implementation and Evaluation in OpenMP, Rust, and Go
type: item
description: This paper presents a study comparing the expressiveness of OpenMP, Rust, and Go in the implementation of five classical concurrent programming problems. The solutions, based on idiomatic approaches, were evaluated in terms of implementation effort, safety, and performance, using the Goal Question Metric (GQM) method. The results highlight the strengths and limitations of each language in different synchronization and resource-sharing contexts, providing insights for selecting appropriate tools in the development of multithreaded systems.
creator: Lucas Braatz Araújo
creator: Daniel Di Domenico
creator: André Rauber Du Bois
creator: Gerson Geraldo H. Cavalheiro
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 1
ending page: 12
doi: 10.5753/sscad.2025.14353

Dependability Analysis of Weather Monitoring Systems Considering Different Redundancy Mechanisms
type: item
description: The increasing occurrence of extreme weather events makes precipitation forecasting essential for disaster mitigation. The Internet of Things (IoT) has enhanced weather monitoring, but ensuring system availability and reliability remains a challenge. This study evaluates the impact of redundancy on system dependability, focusing on availability and reliability. Models were developed to compare a system without redundancy to one incorporating redundancy in critical components. Using Stochastic Petri Nets (SPNs), we analyzed their behavior under different failure and repair scenarios. Results show that redundancy significantly improves availability, reduces the probability of failures, and minimizes downtime, reinforcing its importance in the resilience of climate monitoring systems.
creator: Vinícius Gabriel Pereira Lima
creator: Ermeson Andrade
creator: Danilo R. B. de Araújo
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 13
ending page: 24
doi: 10.5753/sscad.2025.15308

Assessing the Performance and Impact of an Open-Source RTI in Distributed Real-Time Military Simulation
type: item
description: This study evaluates the performance of PORTICO, an open-source Runtime Infrastructure (RTI), for real-time military simulations. The research compares its efficiency with the commercial pRTI solution, analyzing key high-level architecture (HLA) services, including federate interactions and attribute updates. The results demonstrate the viability of the open source solution even beyond conventional usage scenarios, accounting for federation configuration parameters and composition. Furthermore, the work establishes foundations for developing a Minimum Viable Product (MVP) for Modeling and Simulation as a Service (MSaaS) using HLA concepts, enabling scalable and efficient distributed simulations.
creator: João Rodolfo de Oliveira Rosa
creator: Álvaro Luiz Fazenda
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 25
ending page: 36
doi: 10.5753/sscad.2025.15637

Fault Tolerance Implementation in the Lattice Boltzmann Method for Resilient Execution on AWS Ephemeral Instances
type: item
description: Este artigo investiga o desempenho e o custo financeiro do uso de mecanismos de tolerância a falhas no método Lattice Boltzmann (LBM) executado em instâncias efêmeras (spot) da Amazon Web Services (AWS). Duas estratégias de recuperação são implementadas com a extensão ULFM da biblioteca MPI: (i) preemptiva, que suspende a aplicação até a alocação de uma nova instância usando persistência em disco; e (ii) não preemptiva, que permite a continuidade da execução com um número reduzido de instâncias usando persistência em memória. Os resultados indicam que a abordagem não preemptiva proporciona recuperação quase imediata, com um impacto no desempenho pós-falha. Já a abordagem preemptiva evita essa degradação, mas apresenta maior tempo de recuperação. Conclui-se que a estratégia não preemptiva com persistência em memória pode reduzir os custos financeiros em até 32%, mesmo com a ocorrência de falhas.
creator: Rafael Luis Sol Veit Vargas
creator: Vanderlei Munhoz
creator: Márcio Castro
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 37
ending page: 48
doi: 10.5753/sscad.2025.15722

Predicting FaaS Runtime with the Orama Framework Using Machine Learning
type: item
description: One of the significant challenges in Function-as-a-Service (FaaS) is the unpredictability of function runtimes, which can lead to cost overruns and performance degradation when deploying applications across multiple cloud providers. This paper presents a Machine Learning (ML)-based predictor integrated into the Orama Framework, which combines static code metrics (Halstead complexity measures) with empirical performance data to estimate function runtimes directly from the source code. Three neural network architectures (Dense, LSTM, and BLSTM) were evaluated, with the BLSTM achieving the highest accuracy (R2 = 0.91) and a 20% lower MSE compared to the baselines. The predictor is available via API and through a graphical interface within the Orama Framework, supporting deployment planning, multi-cloud comparisons, and cost-performance optimization for serverless applications.
creator: Leonardo Rebouças de Carvalho
creator: Geraldo Pereira Rocha Filho
creator: Aleteia Araujo
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 49
ending page: 60
doi: 10.5753/sscad.2025.15723

Performance Evaluation of N-Body Simulations on AWS with StarPU, OpenMP and MPI Runtime Systems
type: item
description: Cloud Computing provides a cost-effective option for High Performance Computing (HPC) workloads, but brings new challenges. This study evaluates StarPU, a task-based runtime for heterogeneous architectures, in cloud environments by running an N-Body simulation under different cluster configurations and comparing it with traditional HPC runtime systems (OpenMP and MPI). Results show that StarPU excels in single-node setups, especially with GPU acceleration, while scalability varies, struggling with CPU-intensive workloads but performing well in hybrid and GPU-only scenarios. These insights highlight the need for careful infrastructure selection and architectural strategies to ensure good performance in cloud-based HPC.
creator: Nicolas Vanz
creator: Vanderlei Munhoz
creator: Márcio Castro
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 61
ending page: 72
doi: 10.5753/sscad.2025.15831

Buffer Pool Parameter Optimization with Machine Learning in Non-Transactional Environments
type: item
description: A parametrização eficiente de Sistemas Gerenciadores de Banco de Dados (SGBD) é condição essencial para assegurar níveis de serviço estáveis e mitigar picos de latência em ambientes corporativos críticos. Este trabalho apresenta uma solução automatizada de configuração do buffer pool, baseada em uma solução que emprega Aprendizado de Máquina (AM) e Otimização Bayesiana. Assim, aplicou-se Análise Fatorial Exploratória (AFE) associada a K-Means para reduzir a dimensionalidade e eleger métricas representativas. Em seguida, empregou-se regressão LASSO para filtrar variáveis determinantes. Por fim, utilizou-se Regressão com Processo Gaussiano (GPR) e políticas de aquisição bayesiana para gerar recomendações de configuração. Avaliada em um repositório de dados de grande instituição financeira, a solução proporcionou diminuição média de 18% na latência máxima de I/O síncrono. Os resultados evidenciam reprodutibilidade analítica, transparência nas decisões e adaptabilidade a cenários não transacionais, oferecendo uma solução extensível para governança proativa de SGBD em produção.
creator: Eduardo P. Mendizabal
creator: Geraldo P. Rocha Filho
creator: Aleteia Araujo
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 73
ending page: 84
doi: 10.5753/sscad.2025.15841

An Experimental Study of Variable Neighborhood Search for General-Purpose Subgraph Optimization in Parallel Systems
type: item
description: Many network analysis tasks over large graphs involve finding subgraphs that optimize a given scoring function. Scoring functions are defined according to the analysis objective and thus, they can be very diverse (e.g. high density, high reachability, high entropy). In this context we study the Connected Subgraph Optimization Problem (CSOP) in which the goal is to find a subgraph that optimizes a given scoring function within a graph. Such tasks are computationally intensive since (i) they require handling an exponential search space over large graphs and (ii) they involve very skewed and sparse real graphs, which is why algorithm scalability and efficiency are central for feasibility. In this work we provide an experimental characterization of the potential of Variable Neighborhood Search metaheuristic (VNS) in providing a competitive and efficient parallel framework for the CSOP problem. Our results show that VNS offers competitive, near-ideal scalability for accelerating CSOP, but its effectiveness can be limited by expensive scoring functions, branch mispredictions, and both backend and frontend CPU bottlenecks.
creator: Diogo Carvalho
creator: Mayron Moreira
creator: Vinícius Dias
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 85
ending page: 96
doi: 10.5753/sscad.2025.15847

Preventing Out-Of-Memory Errors in Dask through Automated Memory-Aware Chunking
type: item
description: Data-parallel frameworks like Dask partition datasets into chunks for concurrent execution, but choosing suitable chunk dimensions remains challenging: oversized chunks cause Out-Of-Memory (OOM) failures while undersized chunks reduce performance. This paper introduces memory-aware chunking that predicts peak memory from input shapes using linear regression and automatically derives optimal chunk sizes adapted to each operator’s memory requirements. Evaluation on seismic imaging operators across 768 trials shows complete elimination of OOM failures (versus 31.6% failure rate for Dask’s default chunking) and 52% peak memory reduction, enabling reliable distributed processing in memory-constrained environments.
creator: Daniel De Lucca Fonseca
creator: Carlos Alberto Astudillo Trujillo
creator: Edson Borin
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 97
ending page: 108
doi: 10.5753/sscad.2025.15848

Estimating CO2 emissions of distributed applications and platforms with SimGrid_Batsim
type: item
description: This work presents a carbon footprint plugin designed to extend the capabilities of the Batsim simulator by allowing the calculation of CO2 emissions during simulation runs. The goal is to assess the environmental impact associated with task and resource management strategies in simulated environments. The plugin is developed within SimGrid—the underlying simulation framework of Batsim—and computes carbon emissions based on the simulated platform’s energy consumption and carbon intensity factor of the simulated machines. Once implemented, it is integrated into Batsim, ensuring compatibility with existing simulation workflows and enabling researchers to assess the carbon efficiency of their scheduling strategies.
creator: Gabriella Saraiva
creator: Miguel Vasconcelos
creator: Sarita Mazzini Bruschi
creator: Danilo Carastan-Santos
creator: Daniel Cordeiro
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 109
ending page: 120
doi: 10.5753/sscad.2025.15853

Web Simulator for Teaching Computer Architecture with Vector and Cache Support
type: item
description: This article presents NeanderWeb-V, a web-based simulator of the didactic Neander-V architecture, which extends the original Neander with support for vectorization and cache. The tool is accessible directly through web browsers and was designed to support introductory teaching of Computer Architecture and Programming. Its main feature is the exposure of hardware-level parallelism mechanisms, allowing students to observe the impact of the parallel resources in the Neander-V architecture on program performance. Programs can be written, executed, and debugged directly in the browser, in a modular and informative environment. A case study conducted with first-semester Computer Science students indicated high usability and ease of use, reinforcing the pedagogical potential of the tool.
creator: João Pedro R. Linares
creator: Andre Rauber Du Bois
creator: Gerson Geraldo H. Cavalheiro
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 121
ending page: 132
doi: 10.5753/sscad.2025.15861

Interface for Programming Fault-Tolerant Linear Pipelines for Standard C++ MPI
type: item
description: Sistemas de processamento de stream são projetados para operar continuamente e devem ser capazes de se recuperar em caso de falhas. No entanto, programar aplicações de alto desempenho em ambientes distribuídos introduz uma alta complexidade de desenvolvimento. Este trabalho apresenta uma interface de programação que facilita a construção de pipelines lineares tolerantes a falhas para aplicações de processamento de stream em C++. A solução utiliza MPI (Message Passing Interface) para comunicação e o protocolo ABS (Asynchronous Barrier Snapshotting) juntamente com um agente monitor para a etapa de recuperação. Os resultados experimentais indicam uma redução significativa no tempo estimado de desenvolvimento para o programador, com impacto médio de -0.98% até 6.73% na vazão das aplicações. Além disso, o processo de recuperação mitiga o impacto das falhas na vazão do programa.
creator: Eduardo M. Martins
creator: Renato B. Hoffmann
creator: Lucas M. Alf
creator: Dalvan Griebler
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 133
ending page: 144
doi: 10.5753/sscad.2025.15867

Parallelization of Constraint List Generation in Multiple Genetic Sequence Alignments
type: item
description: The alignment of multiple genetic sequences is a crucial step in addressing various problems in the field of Bioinformatics. Since computing optimal alignments is NP-hard, heuristics are commonly employed. Among them, Consistency-Based Alignment yields the best results on average but incurs the highest computational cost. This study presents a parallelization strategy for the core consistency generation steps of this heuristic, along with its most suitable OpenMP scheduling policy. The results obtained with community benchmarks demonstrate the excellent performance of the proposed parallelization, significantly reducing the execution time of consistency generation—from over 4 hours to just under 9 minutes—for alignments of scientifically relevant sizes.
creator: Mario João Jr.
creator: Alexandre C. Sena
creator: Vinod E. F. Rebello
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 145
ending page: 156
doi: 10.5753/sscad.2025.15870

Investigating Contention Management in a Distributed Transactional Memory System
type: item
description: Transactional Memory (TM) is an abstraction for the synchronization of concurrent systems, where shared data is accessed through transactions. To guarantee the consistency of shared data, a transaction only commits if there are no conflicts during its execution, which are resolved by the Contention Manager (CM), who can implement different policies. Distributed Transactional Memory (DTM), in turn, is the adaptation of TM for distributed systems. In this paper, contention management is investigated in DTM systems through eight different policies that were adapted and integrated into Transactional RMI (TRMI), a DTM system based on RMI. Tests were run on two applications, and the results show the impact of the chosen CM policy on their overall performance.
creator: Rafael Rutz dos Santos
creator: Gerson Cavalheiro
creator: Andre Rauber Du Bois
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 157
ending page: 168
doi: 10.5753/sscad.2025.15871

Evaluating Machine Learning Algorithms for Anomaly Detection in Industrial Engines on Edge Devices
type: item
description: Industrial automation and the Internet of Things (IoT) are rapidly evolving, highlighting the need for efficient anomaly detection systems to prevent failures and reduce maintenance and operational costs. This study evaluates the feasibility of running machine learning-based anomaly detection models on edge devices like the Raspberry Pi. Using real sensor data from industrial engines with induced faults, we tested three unsupervised algorithms: LOF, OCSVM, and IF. Metrics such as sensitivity, specificity, inference time, and resource usage were analyzed under different loads. The results show that accurate and low-latency detection is possible on resource-constrained devices, supporting the development of cost-effective and autonomous monitoring systems for industrial environments.
creator: Lucas Araújo
creator: Sérgio Chevtchenko
creator: Danilo Araújo
creator: Ermeson Andrade
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 169
ending page: 180
doi: 10.5753/sscad.2025.15872

Large-Scale RISC-V Processor Verification Using Automated Design Inspection and a Generic Simulation Method
type: item
description: In recent years, RISC-V ISA has gained popularity and research on RISC-V processor verification has increased. However, most studies focus on a small number of cores and rely on implementation-dependent characteristics. To address this limitation, we propose a generic verification framework that monitors high-level processor states through the fetch interface and the register file write port. The execution traces are compared against a reference model. Our framework leverages a Large Language Model to collect the necessary files and signals of new processors, allowing manual code reductions of 85%. We evaluated the framework by testing 21 LLMs in the inspection task and simulating a custom benchmark on 21 processors, identifying 16 bugs across 8 different cores.
creator: Gabriel Gomes
creator: Julio Avelar
creator: Gabriel Oliveira
creator: Enzo Bertoloti
creator: Rodolfo Azevedo
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 181
ending page: 192
doi: 10.5753/sscad.2025.16339

Evaluating Memory Constraints of RISC-V Matrix Accelerators using gem6
type: item
description: Matrix multiplication is a core operation in artificial intelligence workloads, often limited by memory bandwidth in modern computing accelerators. This study explores the architectural integration of a prototype RISC-V matrix extension using the gem5 simulator by modeling various memory hierarchy configurations, ranging from private caches to direct DRAM connections. Results demonstrate that strategic memory hierarchy placement significantly enhances computational throughput and efficiency. Our matrix implementation achieves 1.35x the performance of OpenBLAS using the same architectural state and 87% of the theoretical maximum.
creator: Iago C. Aquino
creator: Casio P. Krebs
creator: Lucas Wanner
creator: Sandro Rigo
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 193
ending page: 204
doi: 10.5753/sscad.2025.16632

Integration and performance analysis of parallel RISC-V architectures
type: item
description: Vector and matrix architectures accelerate workloads by exploiting data-level parallelism and reducing instruction overhead, but using them typically requires manual code changes. This work explores the Hwacha vector coprocessor and Gemmini matrix accelerator, alongside the BOOM superscalar RISC-V core, aiming to automate their activation. The SMR code rewriting tool was extended with libraries for data preparation, movement, and Hwacha/Gemmini activation for GEMV and GEMM. Integrated with Verilator, a simulation environment evaluated performance across seven Polybench kernels. Results show SMR can activate hardware accelerators without modifying the base code, enabling efficient acceleration more easily.
creator: Casio P. Krebs
creator: Guido Araujo
creator: Lucas Wanner
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 205
ending page: 216
doi: 10.5753/sscad.2025.16672

Quantum Machine Learning with Enhanced Autoencoders for Intrusion Detection
type: item
description: Intrusion detection remains a critical challenge in cybersecurity, particularly when dealing with high-dimensional data. In this work, we propose a hybrid quantum machine learning model that integrates a Quantum Autoencoder (QAE) for dimensionality reduction with a Quantum Neural Network (QNN) classifier. The QAE compresses input features into latent states without relying on classical methods such as Principal Component Analysis (PCA), preserving quantum-relevant information. These latent states are then classified using a Variational Quantum Circuit implemented with Qiskit. We systematically evaluated different ansatz architectures and hyperparameters on the NSL-KDD dataset. The best configuration, an EstimatorQNN with EfficientSU2 ansatz (4 repetitions), achieved an F1-score of 0.767, with balanced recall (0.70) and precision (0.84). These results highlight the potential of quantum-enhanced models for addressing the scalability challenges of intrusion detection, representing a step toward applying High-Performance Computing principles to cybersecurity.
creator: Milleny Teixeira de Souza
creator: Matheus Alcântara Souza
creator: Henrique Cota de Freitas
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 217
ending page: 228
doi: 10.5753/sscad.2025.16683

Implementing Cold-Start Reduction Techniques on Globus Compute
type: item
description: This work addresses the cold-start delay issue within Globus Compute, a FaaS solution aimed at scientific workflows. We introduce the Warm-Start Engine, a novel engine for the system which integrates two techniques: an increase in the keepalive time of resources and a new task assignment algorithm. To validate our approach, we conducted a comparative performance analysis of the original system against two new versions, one with only the extended keepalive time and another with both modifications. The results demonstrate that the Warm-Start Engine reduces total workflow execution times by up to 62% and improves individual function execution times by as much as 82%, when compared to the baseline Globus Compute implementation.
creator: João Gabriel Lembo
creator: Alfredo Goldman
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 229
ending page: 241
doi: 10.5753/sscad.2025.16692

Adaptive Detection of Software Aging under Workload Shift
type: item
description: Software aging is a phenomenon that affects long-running systems, leading to progressive performance degradation and increasing the risk of failures. To mitigate this problem, this work proposes an adaptive approach based on machine learning for software aging detection in environments subject to dynamic workload conditions. We evaluate and compare a static model with adaptive models that incorporate adaptive detectors, specifically the Drift Detection Method (DDM) and Adaptive Windowing (ADWIN), originally developed for concept drift scenarios and applied in this work to handle workload shifts. Experiments with simulated sudden, gradual, and recurring workload transitions show that static models suffer a notable performance drop when applied to unseen workload profiles, whereas the adaptive model with ADWIN maintains high accuracy, achieving an F1-Score above 0.93 in all analyzed scenarios.
creator: Rafael José Moura
creator: Maria Gizele Nascimento
creator: Fumio Machida
creator: Ermeson Andrade
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 242
ending page: 253
doi: 10.5753/sscad.2025.16694

MSE: A Matrix Sparsity Extension for RISC-V
type: item
description: The increase in size and computing demands of modern Convolutional Neural Networks (CNNs) requires techniques that can reduce model size and accelerate inference execution. A popular method is pruning, where some model weights are zeroed in a controlled way while maintaining model accuracy. This method can create structured sparsity patterns in the weight matrix, allowing storage in compressed formats. This reduces the model’s memory footprint while maintaining sequential access to the values. In this paper, we propose the Matrix Sparsity Extension (MSE). The proposed RISC-V instruction set extension takes advantage of the sparsity formats generated by pruning, reducing the amount of memory operations needed for matrix multiplication. MSE was able to provide speedups close to 1.24x over the baseline uncompressed operation, reaching 1.75x when the sparse case offers better alignment in cache.
creator: Luc Ribas
creator: Iago Aquino
creator: Isaías Felzmann
creator: Lucas Wanner
creator: Guido Araújo
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 254
ending page: 265
doi: 10.5753/sscad.2025.16697

NUMA-Aware Task Scheduling Strategy Aiming to Reduce Cache Conflicts
type: item
description: This paper presents a NUMA-aware scheduling strategy that reduces cache conflicts by analyzing recent cache index access histories through cumulative distribution functions (CDF). The approach aims to minimize last-level cache (LLC) interference while maintaining load balance across CPUs. We developed a Rust-based simulator to evaluate the strategy under Zipf-distributed workloads, comparing it against the Distributed Intensity Online (DIO) strategy. Results show that the proposed method improves cache hit rates by up to 8.2%, reduces load imbalance up to 18%, and decreases tail latency by 14% relative to DIO. These improvements highlight the potential of fine-grained cacheoblivious scheduling strategies for real-world operating systems.
creator: Thiago de Campos Ribeiro Nolasco
creator: Pedro Henrique Penna
creator: Henrique Cota de Freitas
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 266
ending page: 277
doi: 10.5753/sscad.2025.16702

Can OpenMP Scale Beyond the Node? A Performance Evaluation of Remote Offloading via the MPI Proxy Plugin
type: item
description: We evaluate the MPI Proxy Plugin (MPP), an extension to LLVM/OpenMP that enables remote offloading of target regions via MPI, allowing distributed GPU execution without modifying application code. Using benchmarks on NVIDIA H100 and AMD MI300A nodes, we show that MPP delivers competitive performance compared to traditional MPI+OpenMP, particularly for coarse-grained, compute-intensive workloads. While MPP simplifies development and enables communication–computation overlap, it introduces higher runtime overheads and task granularity requirements. Our results position MPP as a promising step toward unifying shared and distributed heterogeneous programming under the OpenMP model.
creator: Jhonatan Cléto
creator: Guilherme Valarini
creator: Hervé Yviquel
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 278
ending page: 289
doi: 10.5753/sscad.2025.16707

Implementation and Evaluation of Warp Scheduling Policies in Vortex, an Open-Source GPGPU
type: item
description: Despite the popularity of general-purpose GPUs, proprietary architectures limit direct academic experimentation, motivating the use of opensource projects such as Vortex, a GPGPU based on the RISC-V instruction set. This work extends Vortex with two additional warp scheduling strategies: PTA, proposed in this work, which prioritizes warps with a larger number of active threads; and GTO, already established in the literature, which greedily favors active warps until they are stalled. Simulation results using the Rodinia Benchmark reveal that GTO and PTA can reduce execution cycles by up to 26% and 21.5%, respectively, compared to Vortex’s native Round Robin policy.
creator: Samuel A. O. Magalhães
creator: Poliana A. C. Oliveira
creator: Renan A. Marks
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 290
ending page: 301
doi: 10.5753/sscad.2025.16709

A Literature Review on Live Migration in Container-Based Distributed Systems
type: item
description: Task schedulers are a cornerstone of high-performance distributed computing systems, efficiently distributing workloads across multiple nodes to optimize resource utilization and enhance application performance. By balancing computational tasks, they ensure scalability, minimize latency, and maintain system reliability. Live container migration is an emerging technology in the area of task schedulers; it involves making the task of rescheduling containers as fast as possible, thereby reducing the idle time of the system. This article reviews the most relevant works in the area of live migration in container-based distributed systems, identifying the most significant innovations on the subject.
creator: João Pedro Resende Barroso
creator: Aleardo Manacero
creator: Renata Spolon Lobato
creator: Roberta Spolon
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 302
ending page: 313
doi: 10.5753/sscad.2025.16716

Optimization of Sustainable AI Factories through GPU Sharing
type: item
description: O uso de Inteligência Artificial (IA) em larga escala tem crescido rapidamente, aumentando a pressão sobre infraestruturas de computação de alto desempenho (HPC). Em particular, com o surgimento de AI Factories, é necessária a adoção de estratégias eficientes de alocação de recursos para maximizar o throughput e reduzir o consumo energético. Nesse cenário, o compartilhamento de GPUs surge como uma alternativa promissora para melhorar a utilização dos aceleradores e equilibrar desempenho e eficiência energética. Este artigo investiga o impacto do compartilhamento da GPU na eficiência energética durante a inferência de modelos de IA usando o acelerador Intel Data Center GPU Max 1550 no supercomputador de classe exascale Aurora. Avaliamos quatro modelos distintos de IA de diferentes domínios de aplicação, como visão computacional, processamento de linguagem natural e geração de texto. Os resultados mostram que a co-localização de aplicações com perfis de uso de recursos complementares pode reduzir o tempo de execução em até 50% e o consumo de energia em até 43%. Por fim, demonstramos que a alocação de tarefas concorrentes em GPU, baseada na caracterização prévia das aplicações, é uma estratégia promissora para maximizar a eficiência em ambientes HPC.
creator: Matheus M. Costa
creator: Sandro Rigo
creator: Carla Osthoff
creator: Silvio Rizzi
creator: Philippe O. A. Navaux
creator: Arthur Lorenzon
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 314
ending page: 325
doi: 10.5753/sscad.2025.16724

Data Compression for I/O Reduction in High-Performance Seismic Simulations
type: item
description: Simulações sísmicas de alta fidelidade geram grandes volumes de dados, tornando a E/S um gargalo crítico em aplicações de HPC. Este trabalho avalia o impacto da compressão de dados na redução desse gargalo, aplicando ferramentas de compressão sobre conjuntos de dados gerados por um modelo sísmico em diferentes escalas. A análise considera taxa de compressão, tempo de compressão e descompressão, e impacto no tempo total de execução. Os resultados mostram que o fpzip oferece maior compressão, enquanto o lz4 apresenta melhor desempenho em tempo de execução, e o zfp fornece um equilíbrio entre ambos. Também discutimos os desafios para aceleração em GPU, destacando a maturidade da biblioteca nvCOMP para GPUs NVIDIA e a ausência de soluções equivalentes para o ecossistema AMD, indicando caminhos para futuras otimizações em ambientes heterogêneos.
creator: Cristiano A. Künas
creator: Gabriel Freytag
creator: Thiago Araújo
creator: Rodrigo Machado
creator: Bruno Morales
creator: Alexandre Sardinha
creator: Philippe O. A. Navaux
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 326
ending page: 337
doi: 10.5753/sscad.2025.16726

Workflow for Exact Sequence Alignment in High-Performance Computing Systems
type: item
description: O Alinhamento Múltiplo de Sequências (AMS) é uma etapa fundamental na biologia evolutiva molecular, com impacto direto na identificação de marcadores associados a doenças genéticas e infecciosas. A qualidade dos alinhamentos é determinante para a confiabilidade das interpretações biológicas. No entanto, algoritmos exatos para AMS enfrentam limitações quanto ao número de sequências que podem ser processadas, mesmo em ambientes de Processamento de Alto Desempenho (PAD), devido à natureza NP-difícil do problema. Assim, a prática usual envolve a seleção manual de subconjuntos reduzidos de sequências. Neste trabalho, propomos e avaliamos um workflow científico em PAD para AMS com algoritmos exatos, incorporando a seleção automática do subconjunto representativo de sequências, de modo a contornar as restrições das ferramentas disponíveis. Os experimentos, considerando implementações do workflow com PyCOMPSs e com scripts Shell, apresentaram ganhos de 2, 08× e 3, 95×, respectivamente, em relação à execução sequencial das tarefas. Em particular, a implementação com PyCOMPSs mostrou melhor escalabilidade, alcançando 80,8% de ganho no alinhamento de 38 sequências.
creator: Rafael Terra
creator: Kelen Souza
creator: Hiago Rocha
creator: Carla Osthoff
creator: Diego Carvalho
creator: Kary Ocaña
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 338
ending page: 349
doi: 10.5753/sscad.2025.16727

A Weighted Bi-objective Strategy for Executing Scientific Workflows in Containerized Environments
type: item
description: Scientific workflows support the execution of complex simulation-based experiments across heterogeneous computing environments. Containerization technologies, such as Docker, improve portability by encapsulating tasks together with their dependencies. However, they also introduce challenges in resource management, as containers incur additional memory and CPU overhead and may execute concurrently on the same virtual or physical machine. These challenges are particularly critical in memory-constrained environments, where inefficient scheduling can lead to performance degradation or even task failures. To address this issue, we propose a weighted bi-objective scheduling strategy that considers memory consumption and execution time, allowing users to prioritize one objective or achieve a balance between the two. Experimental evaluations with both synthetic and real-world workflows demonstrate that our approach enhances performance and resource utilization.
creator: Wesley Ferreira
creator: Liliane Kunstmann
creator: Yuri Frota
creator: Luan Teylo
creator: Daniel de Oliveira
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 350
ending page: 361
doi: 10.5753/sscad.2025.16730

Federated Outlier Detection for Astronomical Data: Performance Analysis on Commercial Clouds
type: item
description: Astronomical surveys such as the Dark Energy Survey (DES) and the upcoming Legacy Survey of Space and Time (LSST) produce massive volumes of observational data, demanding scalable and efficient data analysis techniques. Among these, Machine Learning (ML) has become a key tool for extracting patterns and detecting anomalies in large astronomical catalogs. However, traditional centralized ML approaches are impractical in this scenario, due to data transfer bottlenecks, storage constraints, and privacy concerns. Federated Learning (FL) offers a decentralized alternative by training models across distributed data sources, reducing transfer costs and preserving data locality. However, configuring and deploying FL workflows is challenging due to client heterogeneity and data distribution. This paper explores the use of FL for outlier detection in large astronomical catalogs, using DES as a proxy for LSST. We emulate FL deployments on Amazon Web Services (AWS) cloud, evaluating various configurations of compute resources. Our results evidence the trade-offs between training time and financial cost, providing insights into the configuration of FL workflows for large-scale LSST data.
creator: Camila Lopes
creator: Wesley Ferreira
creator: Julia Gschwend
creator: Luiz Nicolaci da Costa
creator: Rafael Ferreira da Silva
creator: Marta Mattoso
creator: Aline Paes
creator: Daniel de Oliveira
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 362
ending page: 373
doi: 10.5753/sscad.2025.16731

Fast-Tracking Scalability Analysis: The PaScal Paramount Approach
type: item
description: Scalability analysis of High-Performance Computing applications is essential, but its process is time-consuming and costly. This work introduces PaScal Paramount, a new feature of the PaScal Analyzer tool that accelerates this analysis by estimating the total performance from the execution of a small fraction of the parallel code. Experiments demonstrated that the approach reduces analysis time by up to 89% while maintaining low estimation error and generating performance patterns faithful to the full execution. The feature offers an adjustable trade-off between speed and accuracy, proving to be an effective solution for gaining rapid insights into the behavior of applications, thereby guiding their optimization process.
creator: Reilta Christine Dantas Maia
creator: Samuel Xavier-de-Souza
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 374
ending page: 385
doi: 10.5753/sscad.2025.16734

Analyzing Energy Management Techniques in GPU-Accelerated Applications in an Exascale System
type: item
description: A gestão de energia em sistemas de computação de alto desempenho (HPC) baseados em GPUs é um dos maiores desafios da era Exascale, dada sua influência direta sobre custos operacionais e sustentabilidade ambiental. Entre as técnicas já suportadas pelas arquiteturas modernas, destacam-se o power capping, que limita dinamicamente a potência consumida, e o frequency capping, que impõe tetos estáticos de frequência. Apesar de ambos terem o mesmo objetivo, seus efeitos diferem significativamente conforme o perfil da aplicação, o que torna sua comparação essencial para orientar o uso em escala. Este trabalho apresenta uma avaliação de power capping e frequency capping em três aplicações científicas representativas, executadas no supercomputador Frontier com até 256 GPUs AMD MI250X. Foram exploradas 13 configurações de frequência e 9 limites de potência em cenários de execução em nó único e multinó. Os resultados revelam que: (i) em cargas limitadas por memória, frequency capping em níveis intermediários reduziu o consumo energético em até 25% com impacto inferior a 3% no tempo de execução; (ii) em workloads balanceados entre computação e comunicação, frequency capping manteve desempenho competitivo e superou o power capping em eficiência; e (iii) em cargas intensivas de computação, ambas as técnicas apresentaram ganhos energéticos modestos, mas acompanhados de penalidades significativas de desempenho.
creator: Mariana T. Costa
creator: Antonio Tadeu A. Gomes
creator: Philippe O. A. Navaux
creator: Bronson Messer
creator: Arthur F. Lorenzon
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 386
ending page: 397
doi: 10.5753/sscad.2025.16738

HPC on a budget: on the CPU's impact on dense linear algebra computation with GPUs
type: item
description: While GPU-equipped nodes dominate compute-intensive operations in high-performance computing, the role of host processors in overall system performance remains underexplored, particularly for hardware procurement decisions in resource-constrained environments. This study investigates the influence of the CPU microarchitecture on application performance using an identical modern GPU. We compare two systems with the same NVIDIA RTX4090 GPU but different CPUs: a 2016 Intel Xeon E5-2620v4 and a 2023 Intel Core i9-14900KF. We assess CPU impact on compute-bound workloads using dense Cholesky and LU factorizations from the Chameleon library assisted by the StarPU runtime system. Our findings demonstrate that: (1) CPU influence is negligible with modern GPU accelerators, even if some operations lack GPU implementations; (2) CPU-handled operations are sufficiently small to attenuate performance differences between processor generations; and (3) modern GPUs perform effectively in legacy hardware with minimal penalties. These results suggest selective GPU upgrades offer cost-effective performance improvements without complete system overhauls, providing valuable insights for academic and research institution procurement strategies.
creator: Lucas Barros de Assis
creator: Lucas Mello Schnorr
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 398
ending page: 409
doi: 10.5753/sscad.2025.16739

Optimizing Graph Structures in Persistent Memory for NUMA Architectures
type: item
description: Estruturas de grafos dinâmicos desempenham papel fundamental em aplicações que demandam processamento eficiente de grandes volumes de dados conectados entre si, como redes sociais e sistemas de recomendação. Este trabalho apresenta uma implementação de grafos dinâmicos em memória persistente para ambientes NUMA, explorando o particionamento round-robin e a afinidade explícita de threads para otimizar o processamento. Os experimentos foram conduzidos com dois conjuntos de dados reais de grande escala extraídos do repositório SNAP (Orkut e LiveJournal), permitindo avaliar o impacto das características topológicas dos grafos nas otimizações propostas. Os resultados experimentais demonstraram ganhos expressivos de desempenho, com speedup de até 2,3× em algoritmos como Connected Components(CC), quando comparado à versão original. Por outro lado, evidenciaram-se limitações da abordagem em grafos de baixa densidade e em algoritmos sensíveis à latência de acesso remoto, como BFS, ressaltando a importância de considerar a topologia do grafo na escolha de estratégias de particionamento.
creator: Lucas Spagnol
creator: Bruno Honorio
creator: Otávio Scarparo Souza
creator: Alexandro Baldassin
creator: Emilio Francesquini
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 410
ending page: 421
doi: 10.5753/sscad.2025.16740

A Client-Side Architecture for Enhancing Productivity of Interactive Parallel Scalability Analysis with PaScal Suite
type: item
description: Traditional profilers struggle with scalability analysis. The PaScal Suite’s Viewer tool addresses this, but its server-side architecture creates performance bottlenecks with large datasets. This paper proposes a client-side Single-Page Application (SPA) architecture that transfers all data processing to the browser, enabling fluid, instantaneous analysis. We also introduce a novel hierarchical visualization for comparing code regions, which simplifies diagnostics. The result is an agile platform that enhances the identification of scalability bottlenecks.
creator: Igor Sérgio de França Correia
creator: Samuel Xavier-de-Souza
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 422
ending page: 433
doi: 10.5753/sscad.2025.16744

Efficient MFCC Extraction on FPGA: An Open and Flexible Implementation
type: item
description: This paper presents a set of open-source and parameterizable IPs for MFCC extraction on FPGAs, optimized for edge and real-time applications. The implementation on a Kintex-7 FPGA processes an audio frame in just 56 µs, outperforming a high-performance CPU (AMD Ryzen 7 7700) by 1.62 times. The architecture is resource-efficient, using approximately 5900 LUTs and 31 DSPs, and maintains high precision with a mean absolute error (MAE) of 8.18, validating it as a high-performance, low-power solution for real-time audio analysis.
creator: Julio N. Avelar
creator: Vinicius P. M. Miguel
creator: Tiago S. Zaparoli
creator: Enzo P. Bertoloti
creator: Gabriel Oliveira
creator: Rodolfo Azevedo
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 434
ending page: 445
doi: 10.5753/sscad.2025.16748

Intrusiveness and Scalability of OMPT-Based Tracing Tools for Task-based OpenMP Applications
type: item
description: Task-based parallel programming has become popular for handling irregular parallelism in modern HPC applications. This paradigm requires tailored performance analysis tools, with the OpenMP Tools (OMPT) API being the state-of-the-art for tracing task-based execution events. However, since large-scale applications can generate enormous numbers of tasks, understanding the intrusion of OMPT callbacks and existing tracing tools is crucial. This article proposes a methodology to investigate and compare the intrusiveness and scalability of OMPT-based tracers, evaluating Score-P, Extrae, TiKKi, and custom tracers under various configurations that stress task numbers and registered events. We demonstrate that OMPT-based tracer intrusiveness varies significantly across tools, with some achieving low intrusion and good scalability while others exhibit substantial performance degradation as parallelism increases.
creator: Rayan Raddatz de Matos
creator: Lucas Mello Schnorr
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 446
ending page: 457
doi: 10.5753/sscad.2025.16751

Predictive Modeling of Energy-Delay Product for Submission Optimization in Supercomputers
type: item
description: O consumo de energia em sistemas de Processamento de Alto Desempenho (PAD) tem se tornado um desafio cada vez mais relevante diante do aumento da demanda computacional e da execução de aplicações científicas em larga escala. Fatores relacionados à alocação de recursos, como a quantidade de nós (#N) e o número de threads por nó (#T), impactam diretamente tanto o tempo de execução quanto a eficiência energética, frequentemente avaliada por meio do Energy-Delay Product (EDP). Neste trabalho, propomos uma metodologia baseada no Extra Trees Regressor para recomendar configurações de #N e #T que minimizem o EDP em aplicações paralelas, sem a necessidade de executar a aplicação ou instrumentar seu código. Como estudo de caso, utilizamos a aplicação RAxML e comparamos nossa abordagem com outras quatro estratégias de alocação de recursos em um sistema PAD. Os resultados mostram que a metodologia alcança 81,25% de precisão em relação a melhor estratégia comparada (nosso Oráculo), gerando soluções que diferem, em média, apenas 6,02% do Oráculo e proporcionam uma melhoria de 33,35% no EDP em comparação com a segunda melhor estratégia.
creator: Micaella Coelho
creator: Alexandre Porto
creator: Hiago Mayk G. de A. Rocha
creator: Isabella Muniz
creator: Douglas O. Cardoso
creator: Kary Ocaña
creator: Arthur Lorenzon
creator: Philippe O. A. Navaux
creator: Carla Osthoff
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 458
ending page: 469
doi: 10.5753/sscad.2025.16760

To Pin or not to Pin: That is the question
type: item
description: Hybrid CPU architectures, that combine high-performance and efficiency cores, pose new challenges for serial and parallel applications. With homogeneous processors, problems such as load imbalance, performance predictability, thread migration overhead and energy consumption are mitigated by CPU pinning. However, given the characteristics of hybrid processors, is CPU pinning a solution to the problems posed? Thus, our main goal is to answer the central question: to pin or not to pin? The results indicate that there is no universal answer, as it depends on the application and the execution context.
creator: Guilherme Galante
creator: Marcio Seiji Oyamada
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 470
ending page: 481
doi: 10.5753/sscad.2025.16763

PowerTrackZ: Energy efficiency analysis of access points in wireless networks
type: item
description: The growing demand for wireless networks has driven the need to improve device configurations to ensure performance and energy efficiency. Inadequate access point (AP) configurations increase energy consumption due to conflicts in network management. This work presents an AP analysis tool focused on reducing energy consumption. Based on Zabbix, it correlates metrics such as bandwidth, frequency and channel with energy consumption and interference. In simulation with 18 APs, the tool suggests configurations resulting in a 4.5% reduction in energy consumption and 65% interference reduction. The tool enables efficient use of network resources, maintaining service quality.
creator: Pedro Acácio Rodrigues
creator: João Fabrício Filho
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 482
ending page: 493
doi: 10.5753/sscad.2025.16766

Overcoming Limits in GPU Multi-Partitioning
type: item
description: Multipartitioning is a general-purpose parallel primitive that reorganizes input data into contiguous bins (or buckets), where the function responsible for categorizing each element into a bin is defined by the programmer. Multipartitioning on GPUs, although considered as a parallel primitive in itself, has received little attention in the literature so far. State-of-the-art implementations focus on scenarios with a reduced number of bins (up to 256), also imposing the restriction that this number must be a power of 2. This work aims to present efficient implementations of Multipartitioning on GPUs, exploring different optimized strategies for varying numbers of bins, allowing the algorithm to select, at runtime, the most suitable level of granularity. The algorithm, named mrBin, is capable of processing large numbers of bins (up to 12,288), without imposing restrictions on this number. In other words, more bins, fewer restrictions. The experiments demonstrate that the algorithm surpasses the state of the art for bin counts greater than 64, achieving speedups of up to 2.2x.
creator: Michel B. Cordeiro
creator: Wagner M. Nunan Zola
rights: ##submission.copyrightStatement##
date: 2025-10-28
publication date: 2025-10-28
starting page: 494
ending page: 505
doi: 10.5753/sscad.2025.16780