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Picasso: Memory-Efficient Graph Coloring Using Palettes With Applications in Quantum computing

Picasso: Memory-Efficient Graph Coloring Using Palettes With...

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international symposium on parallel and distributed Processing (IPDPS)

作者： S M Ferdous Reece Neff Bo Peng Salman Shuvo Marco Minutoli Sayak Mukherjee Karol Kowalski Michela Becchi Mahantesh Halappanavar Pacific Northwest National Laboratory Richland WA North Carolina State University Raleigh NC

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

A coloring of a graph is an assignment of colors to vertices such that no two neighboring vertices have the same color. The need for memory-efficient coloring algorithms is motivated by their application in computing clique partitions of graphs arising in quantum computations where the objective is to map a large set of Pauli strings into a compact set of unitaries. We present Picasso, a randomized memory-efficient iterative parallel graph coloring algorithm with theoretical sublinear space guarantees under practical assumptions. The parameters of our algorithm provide a trade-off between coloring quality and resource consumption. To assist the user, we also propose a machine learning model to predict the coloring algorithm’s parameters considering these trade-offs. We provide a sequential and parallel implementation of the proposed *** perform an experimental evaluation on a 64-core AMD CPU equipped with 512 GB of memory and an Nvidia A100 GPU with 40GB of memory. For a small dataset where existing coloring algorithms can be executed within the 512 GB memory budget, we show up to 68× memory savings. On massive datasets, we demonstrate that GPU-accelerated Picasso can process inputs with 49.5× more Pauli strings (vertex set in our graph) and 2,478× more edges than state-of-the-art parallel approaches.

关键词： distributed processing Machine learning algorithms Quantum computing Graphics processing units Color Machine learning Predictive models

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distributed Multi-GPU Community Detection on Exascale computing Platforms

Distributed Multi-GPU Community Detection on Exascale Comput...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Naw Safrin Sattar Hao Lu Feiyi Wang Mahantesh Halappanavar Oak Ridge National Laboratory Oak Ridge TN USA Pacific Northwest National Laboratory Richland WA USA

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Community detection is a fundamental operation in graph mining, and by uncovering hidden structures and patterns within complex systems it helps solve fundamental problems pertaining to social networks, such as information diffusion, epidemics, and recommender systems. Scaling graph algorithms for massive networks becomes challenging on modern distributed-memory multi-GPU (Graphics Processing Unit) systems due to limitations such as irregular memory access patterns, load imbalances, higher communication-computation ratios, and cross-platform support. We present a novel algorithm HiPDPL-GPU (distributed parallel Louvain) to address these challenges. We conduct experiments involving different partitioning techniques to achieve an optimized performance of HiPDPL-GPU on the two largest supercomputers: Frontier and Summit. Remarkably, HiPDPL-GPU processes a graph with 4.2 billion edges in less than 3 minutes using 1024 GPUs. Qualitatively, the performance of HiPDPL-GPU is similar or better compared to other state-of-the-art CPU- and GPU-based implementations. While prior GPU implementations have predominantly employed CUDA, our first-of-its-kind implementation for community detection is cross-platform, accommodating both AMD and NVIDIA GPUs.

关键词： Epidemics distributed processing Exascale computing Image edge detection Conferences Graphics processing units Partitioning algorithms

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Weak Amnesiac Flooding 20

Weak Amnesiac Flooding

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20th international symposium on parallel and distributed computing (ISPDC)

作者： Bayramzadeh, Zahra Kshemkalyani, Ajay D. Molla, Anisur Rahaman Sharma, Gokarna Kent State Univ Kent OH 44242 USA Univ Illinois Chicago IL 60607 USA Indian Stat Inst Kolkata 700108 India

ISBN: (纸本)9781665432818

Flooding is a fundamental concept in distributed computing. In flooding, typically, a node forwards a message to its neighbors for the first time when it receives a message. Later if the node receives the same message again, it simply ignores the message and does not forward it. The nodes store a "message record" to ensure that the same message is not forwarded again. Hussak and Trehan introduced amnesiac flooding where nodes do not require to keep the message record. They established a surprising result that the amnesic flooding of a single (k = 1) message starting from some source node always terminates in bipartite graphs in e rounds and in non-bipartite graphs in [e + 1;e + D + 1] rounds, where e is the eccentricity of the source node and D is the diameter of the graph. Recently, Hussak and Trehan introduced dynamic amnesiac flooding initiated in possibly multiple rounds with possibly multiple (k > 1) messages from possibly multiple source nodes. They showed that the partial-send case where a node only sends a message to neighbours from which it did not receive any message in the previous round and the ranked full-send case where a node sends some highest ranked message to all neighbors from which it did not receive that message in the previous round, both terminate. However, they showed that the unranked full-send case, where a node sends some random message (not necessarily the highest ranked message) to all the neighbors from which it did not receive that message in the previous round, does not terminate. In this paper, we show that the unranked full-send case also terminates, provided that diameter D is known to graph nodes. We further show that the termination time is D . (2k - 1) rounds in bipartite graphs and (2D + 1) . (2k 1) rounds in non-bipartite

关键词： distributed Algorithms Classic Flooding Amnesiac Flooding Termination Time Complexity Memory

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Performance Analysis of Deep Learning Workloads on a Composable System

Performance Analysis of Deep Learning Workloads on a Composa...

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： El Maghraoui, Kauotar Herger, Lorraine M. Choudary, Chekuri Tran, Kim Deshane, Todd Hanson, David IBM TJ Watson Res Ctr New York NY 10562 USA IBM Syst Houston TX USA

ISBN: (纸本)9781665435772

A composable infrastructure is defined as resources, such as compute, storage, accelerators and networking, that are shared in a pool and that can be grouped in various configurations to meet application requirements. This freedom to 'mix and match' resources dynamically allows for experimentation early in the design cycle, prior to the final architectural design or hardware implementation of a system. We describe the design of an enterprise composable infrastructure that we have implemented and evaluate the impact of resource dis-aggregation on representative deep learning benchmarks.

关键词： Deep learning Training distributed processing High performance computing Conferences Benchmark testing Hardware

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RAW 2024 Invited Talk-6: Reconfigurable Architectures for High-Performance computing

RAW 2024 Invited Talk-6: Reconfigurable Architectures for Hi...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Kentaro Sano RIKEN

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

At RIKEN Center for Computational Science (R-CCS), we have been developing an experimental FPGA Cluster named “ESSPER (Elastic and Scalable System for high-PErformance Reconfigurable computing),” which is a research platform for reconfigurable HPC. ESSPER is composed of sixteen Intel Stratix 10 SX FPGAs which are connected to each other by a dedicated 100Gbps inter-FPGA network. We have developed our own Shell (SoC) and its software APIs for the FPGAs supporting inter-FPGA communication. In this talk, I introduce ESSPER's concept, designed system stack with hardware and software, and lessons we learned as issues of FPGA-based HPC. I also talk about our recent research activity on coarse-grained reconfigurable array (CGRA) as another reconfigurable architecture to address the issues as well as our future prospects for reconfigurable HPC in the future.

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ABET Accreditation: A Way Forward for PDC Education

ABET Accreditation: A Way Forward for PDC Education

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Aly, Sherif G. Harmanani, Haidar Raj, Rajendra K. Sharafeddine, Sanaa Amer Univ Cairo Dept Comp Sci & Engn Cairo Egypt Lebanese Amer Univ Dept Comp Sci & Math Beirut Lebanon Rochester Inst Technol Dept Comp Sci Rochester NY 14623 USA

ISBN: (纸本)9781665435772

With parallel and distributed computing (PDC) now wide-spread, modern computing programs must incorporate PDC within the curriculum. ACM and IEEE Computer Society's Computer Science curricular guidelines have recommended exposure to PDC concepts since 2013. More recently, a variety of initiatives have made PDC curricular content, lectures, and labs freely available for undergraduate computer science programs. Despite these efforts, progress in ensuring computer science students graduate with sufficient PDC exposure has been uneven. This paper discusses the impact of ABET's revised criteria that have required exposure to PDC to achieve accreditation for computer science programs since 2018. The authors reviewed 20 top ABET-accredited computer science programs and analyzed how they covered the required PDC components in their curricula. Using their own institutions as case studies, the authors examine in detail how three different ABET-accredited computer science programs covered PDC using different approaches, yet meeting the PDC requirements of these ABET criteria. The paper also shows how ACM/IEEE Computer Society curricular guidelines for computer engineering and software engineering programs, along with ABET accreditation criteria, can cover PDC.

关键词： parallel and distributed computing computing programs curricular guidelines ABET accreditation

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Introduction to Computational Quantum Chemistry for Computer Scientists

Introduction to Computational Quantum Chemistry for Computer...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Yasuaki Ito Satoki Tsuji Haruto Fujii Kanta Suzuki Nobuya Yokogawa Koji Nakano Akihiko Kasagi Graduate School of Advanced Science and Technology Hiroshima University Higashi-Hiroshima JAPAN Computing Laboratory Fujitsu Limited Kawasaki JAPAN

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Computational quantum chemistry is a theoretical approach that utilizes the principles of quantum mechanics to perform computations on the electronic structure of molecules and atoms. This method enables the prediction of properties such as molecular structure, energy, electron orbitals, and electron density. In the field of the first principles, or ab initio, which relies solely on computation and not on experimental data, mathematical understanding allows even those without a background in chemistry to perform molecular orbital computations. The computation of quantum chemistry is computationally intensive, making them highly applicable in the field of high-performance computing. However, initiating quantum chemistry can be challenging for newcomers due to the numerous specialized terms and complex equations from quantum mechanics and chemistry. Therefore, this paper aims to provide a clear and accessible compilation of the fundamental and essential algorithm of the Hartree- Fock method, from theory to implementation. The goal is to offer the minimum required knowledge, in an approachable manner, serving as a starting point for those interested in computational quantum chemistry.

关键词： distributed processing Quantum chemistry High performance computing Conferences Quantum mechanics Atoms Orbits

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Movie Recommender System Based on Generative AI

Movie Recommender System Based on Generative AI

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parallel computing and distributed Systems (PCDS), international symposium on

作者： Carmen Pei Ling Tung Su-Cheng Haw Wan-Er Kong Palanichamy Naveen Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

Movie recommender systems have revolutionized how people discover and experience movies using sophisticated algorithms, such a system generates personalized recommendations to assist users in reaching the movies that precisely fit their tastes. The traditional movie recommender systems lacks of diversity, unable to capture complex user preferences, and suffers from the cold-start issues. In this paper, we utilize the generative AI, specifically with Variational Autoencoders (VAE), to solve these problems. By combining VAEs, the system aims to produce more rich and individualized recommendations. VAE mitigates these issues by enabling the generation of meaningful item representations, promoting diversity in recommendations, and capturing the complexity of user preferences in a continuous latent space. The implementation is structured as follows: first, review the generative AI in movie recommender systems; second, implement the chosen generative AI approach; and finally, conduct the performance metrics evaluation of the selected generative AI approach. Ultimately, this research will enable the system to offer an achievement of an interactive, changing, and more personalized movie-discovering experience through the amazing power of generative AI.

关键词： Measurement Generative AI Reviews Diversity reception Refining Prototypes parallel processing Motion pictures History Recommender systems

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Improved Deterministic Connectivity in Massively parallel Computation 36

Improved Deterministic Connectivity in Massively Parallel Co...

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36th international symposium on distributed computing, DISC 2022

作者： Fischer, Manuela Giliberti, Jeff Grunau, Christoph ETH Zürich Switzerland

ISBN: (纸本)9783959772556

A long line of research about connectivity in the Massively parallel Computation model has culminated in the seminal works of Andoni et al. [FOCS'18] and Behnezhad et al. [FOCS'19]. They provide a randomized algorithm for low-space MPC with conjectured to be optimal round complexity O(log D + log log m/n n) and O(m) space, for graphs on n vertices with m edges and diameter D. Surprisingly, a recent result of Coy and Czumaj [STOC'22] shows how to achieve the same deterministically. Unfortunately, however, their algorithm suffers from large local computation time. We present a deterministic connectivity algorithm that matches all the parameters of the randomized algorithm and, in addition, significantly reduces the local computation time to nearly linear. Our derandomization method is based on reducing the amount of randomness needed to allow for a simpler efficient search. While similar randomness reduction approaches have been used before, our result is not only strikingly simpler, but it is the first to have efficient local computation. This is why we believe it to serve as a starting point for the systematic development of computation-efficient derandomization approaches in low-memory MPC. © Manuela Fischer, Jeff Giliberti, and Christoph Grunau.

关键词： MapReduce

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SVGDoc: A CRDT for Collaborative Vector Graphics Editing

SVGDoc: A CRDT for Collaborative Vector Graphics Editing

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parallel computing and distributed Systems (PCDS), international symposium on

作者： Bryn Ghiffar Jason Jeremy Wijadi Ida Bagus Kerthyayana Manuaba Computer Science Department School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

Conflict-free Replicated Data Types (CRDTs) are a class of algorithms that allow decentralized editing of documents as data structures in which concurrent operations are commutative, while there has been extensive research on generic CRDTs which allow CRDTs to be modeled according to application data. Current generic CRDTs use a CRDT map or an operation log which lack a tree structure with support for move operations to represent a vector graphics document or does not preserve updates that are concurrent to deletions. In this paper, a set of concurrent editing scenarios that are frequently made by user's of collaborative vector graphics editing are described. A CRDT for collaborative vector graphics editing which cover these scenarios named SVGDoc is then introduced. The main idea of SVGDoc is to use the UWMap (Update-Wins Map) as described by Preguica to allow updates to be preserved when concurrently made to deletions in combination with the move operation log described by Kleppmann et al to allow move operations among nodes in a tree. SVGDoc is also tested through unit testing and further validated by applying it to a minimal collaborative vector graphics editor.

关键词： Graphics Runtime Merging Memory management Collaboration parallel processing Data structures Vectors Time complexity Testing

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