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ISC High Performance 2024 Research Paper Proceedings (39th International Conference)

作者： Daniel Nichols Aniruddha Marathe Harshitha Menon Todd Gamblin Abhinav Bhatele Department of Computer Science University of Maryland College Park MD USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore CA USA Livermore Computing Lawrence Livermore National Laboratory Livermore CA USA

Parallel programs in high performance computing (HPC) continue to grow in complexity and scale in the exascale era. The diversity in hardware and parallel programming models make developing, optimizing, and maintaining parallel software even more burdensome for developers. One way to alleviate some of these burdens is with automated development and analysis tools. Such tools can perform complex and/or remedial tasks for developers that increase their productivity and decrease the chance for error. Until recently, such tools for code development and performance analysis have been limited in the complexity of tasks they can perform, especially for parallel programs. However, with recent advancements in language modeling, and the availability of large amounts of open-source code related data, these tools have started to utilize predictive language models to automate more complex tasks. In this paper, we show how large language models (LLMs) can be applied to tasks specific to high performance and scientific codes. We introduce a new dataset of HPC and scientific codes and use it to fine-tune several pre-trained models. We compare several pre-trained LLMs on HPC-related tasks and introduce a new model, HPC-Coder, fine-tuned on parallel codes. In our experiments, we show that this model can auto-complete HPC functions where generic models cannot, decorate for loops with OpenMP pragmas, and model performance changes in scientific application repositories as well as programming competition solutions.

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Enhanced prairie dog optimization with Levy flight and dynamic opposition-based learning for global optimization and engineering design problems

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Neural computing and Applications 2024年第19期36卷 11137-11170页

作者： Biswas, Saptadeep Shaikh, Azharuddin Ezugwu, Absalom El-Shamir Greeff, Japie Mirjalili, Seyedali Bera, Uttam Kumar Abualigah, Laith Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 South Africa Faculty of Natural and Agricultural Sciences School of Computer Science and Information Systems North-West University Vanderbijlpark South Africa Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia College of Engineering Yuan Ze University Taoyuan32003 Taiwan MEU Research Unit Middle East University Amman11831 Jordan

This study proposes a new prairie dog optimization algorithm version called EPDO. This new version aims to address the issues of premature convergence and slow convergence that were observed in the original PDO algorithm. To improve performance, several modifications are introduced in EPDO. First, a dynamic opposite learning strategy is employed to increase the diversity of the population and prevent premature convergence. This strategy helps the algorithm avoid falling into local optima and promotes global optimization. Additionally, the Lévy dynamic random walk technique is utilized in EPDO. This modified Lévy flight with random walk reduces the algorithm’s running time for the test function’s ideal value, accelerating its convergence. The proposed approach is evaluated using 33 benchmark problems from CEC 2017 and compared against seven other comparative techniques: GWO, MFO, ALO, WOA, DA, SCA, and RSA. Numerical results demonstrate that EPDO produces good outcomes and performs well in solving benchmark problems. To further validate the results and assess reliability, the authors employ average rank tests, the measurement of alternatives, and ranking according to the compromise solution (MARCOS) method, as well as a convergence report of EPDO and other algorithms. Furthermore, the effectiveness of the EPDO algorithm is demonstrated by applying it to five design problems. The results indicate that EPDO achieves impressive outcomes and proves its capability to address practical issues. The algorithm performs well in solving benchmark and practical design problems, as supported by the numerical results and validation methods used in the study. © The Author(s) 2024.

关键词： Global optimization

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Informed Machine Learning for Optimizing Melt Spinning Processes

Informed Machine Learning for Optimizing Melt Spinning Proce...

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Artificial Intelligence (CAI), IEEE Conference on

作者： Viny Saajan Victor Manuel Ettmüller Andre Schmeißer Heike Leitte Simone Gramsch Transport Processes Department Fraunhofer ITWM Kaiserslautern Germany Department of Visualization and Scientific Computing Rhineland-Palatinate Technical University Kaiserslautern Germany Department of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt Germany

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

Industrial textiles have increasingly become a major part of our day-to-day lives owing to their various desirable properties. Melt spinning processes are a primary and integral part of the production of these textiles. Optimizing the spinning process while maintaining desirable quality is one of the key challenges for the textile industry. Although numerical models, which are digital twins of physical processes, are often used in optimization, they tend to be computationally expensive for complex scenarios. Hence, in this paper, we utilize machine learning to facilitate the optimization of melt spinning processes. We present a novel, reliable, and informed machine-learning model that is both data-and physics-driven. We further demonstrate the capability of this model to accelerate the optimization and analysis of melt spinning processes.

关键词： Computational modeling Machine learning Differential equations Numerical models Spinning Reliability Isothermal processes

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Enhancing the Performance of AlphaFold Through Modified Storage Method and Optimization of HHblits on TSUBAME3.0 Supercomputer

Enhancing the Performance of AlphaFold Through Modified Stor...

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computer science, computer Engineering, & Applied computing (CSCE), Congress in

作者： Hayato Fujita Akihiro Nomura Toshio Endo Masakazu Sekijima Department of Computer Science Tokyo Institute of Technology Yokohama Japan Global Scientific Information and Computing Center (GSIC) Tokyo Institute of Technology Yokohama Japan

Knowledge of the three-dimensional structures of proteins is vital for understanding their functions. This knowledge can also serve as a basis for understanding the various functions of the human body. The three-dimensional structures of proteins have been experimentally ascertained from the known amino acid sequence of proteins. To reduce the time and cost of experiments, computer-based methods such as AlphaFold have been proposed. AlphaFold uses existing tools such as HHblits to obtain multiple sequence alignments (MSAs) from huge G protein (sequence) databases, such as BFD. However, HHblits requires a long execution time, as it has to perform many I/O operations. The execution time of HHblits differs significantly depending on the type of storage used to store the protein (sequence) database. Notably, the execution time of HHblits when using TSUBAME3.0 Lustre storage area with default settings differs significantly from that when storage with stripe settings is used. Therefore, we modified the storage method of the protein (sequence) database that can be selected on TSUBAME3.0 and measured the execution time of HHblits based on the storage method selected. Further-more, we profiled the various bottlenecks of HHblits, and based on the results, tuned the number of parallel processes, modified the database arrangement, and optimized sorting. Furthermore, we made the tool execution asynchronous due to the data dependency between the MSA acquisition tools of AlphaFold. Consequently, we succeeded in shortening the execution time by half, on average, when predicting a three-dimensional structure from a single amino acid sequence on TSUBAME3.0. The modifications proposed in this study to accelerate the process have already been pull requested at the following URLs: HHblits (https://***/soedinglab/hh-suite/pull/307) and AlphaFold (https://***/deepmind/alphafold/pull/399).

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High-performance computing at a crossroads

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science (New York, N.Y.) 2025年第6736期387卷 829-831页

作者： Deelman, Ewa Dongarra, Jack Hendrickson, Bruce Randles, Amanda Reed, Daniel Seidel, Edward Yelick, Katherine Information Sciences Institute University of Southern California Marina del Rey CA United States Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN United States Oak Ridge National Laboratory Oak Ridge TN United States Department of Mathematics University of Manchester Manchester United Kingdom Computing Directorate Lawrence Livermore National Laboratory Livermore CA United States Department of Biomedical Engineering Duke University Durham NC United States Scientific Computing and Imaging Institute University of Utah Salt Lake City UT United States Office of the President University of Wyoming Laramie WY United States Department of Electical Engineering and Computer Sciences University of California Berkeley Berkeley CA United States

Long-term plans and comprehensive vision are needed.

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ML-based Modeling to Predict I/O Performance on Different Storage Sub-systems

ML-based Modeling to Predict I/O Performance on Different St...

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International Conference on High Performance computing

作者： Yiheng Xu Pranav Sivaraman Hariharan Devarajan Kathryn Mohror Abhinav Bhatele Palantir Technologies Inc. Denver USA Department of Computer Science University of Maryland College Park USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore USA

ISBN: (数字)9798331509095

ISBN: (纸本)9798331509101

Parallel applications can spend a significant amount of time performing I/O on large-scale supercomputers. Fast near-compute storage accelerators called burst buffers can reduce the time a processor spends performing I/O and mitigate I/O bottlenecks. However, determining if a given application could be accelerated using burst buffers is not straightforward even for storage experts. The relationship between an application's I/O characteristics (such as I/O volume, processes involved, etc.) and the best storage sub-system for it can be complicated. As a result, adapting parallel applications to use burst buffers efficiently is a trial-and-error process. In this work, we present a Python-based tool called PrismIO that enables programmatic analysis of I/O traces. Using PrismIO, we identify performance bottlenecks when using burst buffers and parallel file systems, and explain why certain I/O patterns perform poorly. Further, we use machine learning to model the relationship between I/O characteristics and file system selections. We use IOR, an I/O benchmark to gather performance data for training the machine learning model. Our model can predict the better performing storage system for unseen IOR scenarios with an accuracy of 94.47% and for four real applications with an accuracy of 95.86%.

关键词： Training Accuracy File systems Computational modeling High performance computing Machine learning Predictive models Benchmark testing Data models Supercomputers

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LimonDroid: a system coupling three signature-based schemes for profiling Android malware

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Iran Journal of computer science 2021年第2期4卷 95-114页

作者： Tchakounté, Franklin Ngassi, Roger Corneille Ndjeumou Kamla, Vivient Corneille Udagepola, Kalum Priyanath Department of Mathematics and Computer Science Faculty of Science University of Ngaoundéré Ngaoundéré Cameroon Department of Mathematics and Computer Science National School of Agro-Industrial Science University of Ngaoundéré Ngaoundéré Cameroon Department of Information and Computing Sciences Scientific Research Development Institute of Technology Loganlea Australia

Android remains an interesting target to attackers due to its openness. A contribution in the literature consists of providing similarity measurement such as fuzzy hashing to fight against code obfuscation techniques. Research works in this approach suffer from limited signature database. This work combines fuzzy hashing with YARA rules and VirusTotal signature-based schemes, to improve and consistency of the signature database. It is proposed LimonDroid, an Android system, which mimics Limon, a Desktop security tool that includes such schemes. LimonDroid has been tested with 341 malicious and 300 benign applications on a database of 12925 fuzzy-hashed malware signatures, 62 YARA malware families’ patterns and VirusTotal engine. Our approach gives a true-positive rate of 97.36%, a true negative rate of 98.33% and an accuracy of 97.82%. A comparison with similarity-based solutions reveals that LimonDroid is more efficient for users. The objective is not to propose a detection approach better than those in the literature. Instead, we aim at establishing a robust signature database able to identify malicious trends in Android apps. © Springer Nature Switzerland AG 2020.

关键词： Android malware Detection Fuzzy hashing Limon sandbox Similarity Static analysis VirusTotal YARA

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HAPIC: A Scalable, Lightweight and Reactive Cache for Persistent-Memory-Based Index

HAPIC: A Scalable, Lightweight and Reactive Cache for Persis...

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IEEE International Conference on computer-Aided Design

作者： Chih-Ting Lo Yun-Chih Chen Yuan-Hao Chang Tei-Wei Kuo Department of Computer Science and Information Engineering National Taiwan University Academia Sinica Institute of Information Science Taiwan Department of Computer Science Mohamed bin Zayed University of Artificial Intelligence NTU High Performance and Scientific Computing Center National Taiwan University

In-memory index delivers low-latency responses for data services. It has been ported to high-capacity persistent memory (PM) to accommodate more data. However, read-heavy, extremely-skewed, and highly-dynamic workloads can suffer from degraded performance on PM-based indexes. We present HAPIC, a scalable cache over PM-based indexes to capture the constantly-changing query hotspots in skewed workloads. HAPIC embodies the data access frequency gradient in a hierarchy of hash tables to efficiently identify hotspots and reacts quickly to workload changes with epoch-based promotion. Compared with the state-of-the-art strategy, HAPIC reacts to hotspot shifts significantly faster, with up to 14% higher stable read throughput, 26% lower median latency, and 13% lower P99 latency.

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Optimizing Irregular Communication with Neighborhood Collectives and Locality-Aware Parallelism

arXiv

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arXiv 2023年

作者： Collom, Gerald Li, Rui Peng Bienz, Amanda Department of Computer Science University of New Mexico Albuquerque United States Center for Advanced Scientific Computing Lawrence Livermore National Laboratory Livermore United States

Irregular communication often limits both the performance and scalability of parallel applications. Typically, applications individually implement irregular messages using point-to-point communications, and any optimizations are added directly into the application. As a result, these optimizations lack portability. There is no easy way to optimize point-to-point messages within MPI, as the interface for single messages provides no information on the collection of all communication to be performed. However, the persistent neighbor collective API, released in the MPI 4 standard, provides an interface for portable optimizations of irregular communication within MPI libraries. This paper presents methods for optimizing irregular communication within neighborhood collectives, analyzes the impact of replacing point-to-point communication in existing codebases such as Hypre BoomerAMG with neighborhood collectives, and finally shows an up to 1.32x speedup on sparse matrix-vector multiplication within a BoomerAMG solve through the use of our optimized neighbor collectives. The authors analyze multiple implementations of neighborhood collectives, including a standard implementation, which simply wraps standard point-to-point communication, as well as multiple implementations of locality-aware aggregation. All optimizations are available in an open-source codebase, MPI Advance, which sits on top of MPI, allowing for optimizations to be added into existing codebases regardless of the system MPI install. © 2023, CC BY.

关键词： Application programming interfaces (API)

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Adaptive Power Control Aware Depth Routing in Underwater Sensor Networks

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computers, Materials & Continua 2021年第10期69卷 1301-1322页

作者： Ghufran Ahmed Saiful Islam Ihsan Ali Isra Adil Hayder Abdelmuttlib Ibrahim Abdalla Ahmed Muhammad Talha Sultan S.Alshamrani Ag Asri Ag Ibrahim Department of Computer Science FAST National University of Computer and Emerging Sciences(NUCES)KarachiPakistan Dr.A.Q.Khan Institute of Computer Sciences and Information Technology KahutaPakistan Faculty of Computer Science and Information Technology University of MalayaKuala Lumpur50603Malaysia Department of Computer Science COMSATS University IslamabadIslamabadPakistan Deanship of Scientific Research King Saud UniversityRiyadhSaudi Arabia Department of Information Technology College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia Faculty of Computing and Informatics Universiti Malaysia SabahKota KinabaluSabahMalaysia

Underwater acoustic sensor network(UASN)refers to a procedure that promotes a broad spectrum of aquatic *** can be practically applied in seismic checking,ocean mine identification,resource exploration,pollution checking,and disaster *** confronts many difficulties and issues,such as low bandwidth,node movements,propagation delay,3D arrangement,energy limitation,and high-cost production and arrangement costs caused by antagonistic underwater *** wireless sensor networks(UWSNs)are considered a major issue being encountered in energy management because of the limited battery power of their ***,the harsh underwater environment requires vendors to design and deploy energy-hungry devices to fulfil the communication requirements and maintain an acceptable quality of ***,increased transmission power levels result in higher channel interference,thereby increasing packet *** the facts mentioned above,this research presents a controlled transmission power-based sparsity-aware energy-efficient clustering in *** contributions of this technique is ***,it uses the adaptive power control mechanism to utilize the sensor nodes’battery and reduce channel interference ***,thresholds are defined to ensure successful ***,clustering can be implemented in dense areas to decrease the repetitive transmission that ultimately affects the energy consumption of nodes and interference ***,mobile sinks are deployed to gather information locally to achieve the previously mentioned *** suggested protocol is meticulously examined through extensive simulations and is validated through comparison with other advanced UWSN *** show that the suggested protocol outperforms other procedures in terms of network lifetime and packet delivery ratio.

关键词： UWSNs UASNs QoS

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