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Optimizing Forward Wavefield Storage Leveraging High -Speed Storage Media

Optimizing Forward Wavefield Storage Leveraging High -Speed ...

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1st international Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Speglich, Joao Kukreja, Navjot Bisbas, George Saraiva, Atila Huckelheim, Jan Luporini, Fabio Washbourne, John SENAI CIMATEC Supercomp Ctr Salvador BA Brazil Univ Liverpool Liverpool Merseyside England Imperial Coll London London England Argonne Natl Lab 9700 S Cass Ave Argonne IL 60439 USA Devito Codes London England Chevron Tech Ctr Houston TX USA

ISBN: (纸本)9798350364613;9798350364606

Full-Waveform Inversion (FWI) is a commonly used adjoint-based optimization technique in geosciences. Similar to other adjoint-based optimizations, MI involves gradient calculations that impose a substantial memory footprint. This memory footprint is often considered the primary bottleneck in this FWI process and has been the subject of extensive research. Previous studies have explored various approaches to mitigate this issue, including optimal checkpointing strategies, lossy compression, subsampling in time, and other methods aimed at reducing the memory requirements. These investigations have typically operated under the assumption that writing the data to disk would result in significant and unacceptable performance degradation, leading to compromises in precision or increased computational costs to conserve memory resourcesin this work, we challenge this assumption in light of recent advancements in high -bandwidth storage, particularly the utilization of Non-Volatile Memory Express solid-state drives (NVMe SSDs). We provide empirical evidence that, with specific combinations of modern hardware, it is indeed feasible to write temporary data to disk during the forward propagation phase and subsequently read it back during the adjoint computation without experiencing substantial performance degradation.

关键词： waveform inversion Adjoint-based optimization Gradient calculations Memory footprint Large-scale inverse problems parallel computing Non-volatile memory express solid-state drives (NVMe SSDs) high-speed storage

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Performance Evaluations of Noisy Approximate Quantum Fourier Arithmetic 36

Performance Evaluations of Noisy Approximate Quantum Fourier...

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36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Basili, Robert Qian, Wenyang Tang, Shuo Castellino, Austin Eshaghian-Wilner, Mary Khokhar, Ashfaq Luecke, Glenn Vary, James P. Iowa State Univ Dept Math Ames IA 50011 USA Iowa State Univ Dept Phys & Astron Ames IA 50011 USA Iowa State Univ Ames IA USA Iowa State Univ Dept Elect & Comp Engn Ames IA USA

ISBN: (纸本)9781665497473

The Quantum Fourier Transform (QFT) grants competitive advantages, especially in resource usage and circuit approximation, for performing arithmetic operations on quantum computers, and offers a potential route towards a numerical quantum-computational paradigm. In this paper, we utilize efficient techniques to implement QFT-based integer addition and multiplications. These operations are fundamental to various quantum applications including Shor's algorithm, weighted sum optimization problems in data processing and machine learning, and quantum algorithms requiring inner products. We carry out performance evaluations of these implementations based on IBM's superconducting qubit architecture using different compatible noise models. We isolate the sensitivity of the component quantum circuits on both one-/two-qubit gate error rates, and the number of the arithmetic operands' superposed integer states. We analyze performance, and identify the most effective approximation depths for quantum add and quantum multiply within the given context. We observe significant dependency of the optimal approximation depth on the degree of machine noise and the number of superposed states in certain performance regimes. Finally, we elaborate on the algorithmic challenges - relevant to signed, unsigned, modular and non-modular versions - that could also be applied to current implementations of QFT-based subtraction, division, exponentiation, and their potential tensor extensions. We analyze performance trends in our results and speculate on possible future development within this computational paradigm.

关键词： Quantum computing Computational Models Approximate Quantum Fourier Transform Quantum Arithmetic

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Lock-free Computation of PageRank in Dynamic Graphs

Lock-free Computation of PageRank in Dynamic Graphs

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1st international Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Sahu, Subhajit Kothapalli, Kishore Eedi, Hemalatha Peri, Sathya Int Inst Informat Technol Hyderabad Hyderabad India JNTUH Coll Engn Hyderabad Hyderabad India Indian Inst Technol Hyderabad Hyderabad India

ISBN: (纸本)9798350364613;9798350364606

PageRank is a metric that assigns importance to the vertices of a graph based on its neighbors and their scores. Recently, there has been increasing interest in computing PageRank on dynamic graphs, where the graph structure evolves due to edge insertions and deletions. However, traditional harrier-based approaches for updating PageRanks encounter significant wait times on certain graph stnictures, leading to high overall runtimes. Additionally, the growing trend of multicore architectures with increased core counts has raised concerns about random thread delays and failures. In this study, we propose a lock-free algorithm for updating PageRank scores on dynamic graphs. First, we introduce our Dynamic Frontier (DF) approach, which identifies and processes vertices likely to change PageRanks with minimal overhead. Subsequently, we integrate DF with our lock-free and fault -tolerant PageRank (Alg. DFLF), incorporating a helping mechanism among threads between its Iwo phases. Experimental results demonstrate that Alg. Mix not only eliminates waiting times at iteration barriers but also withstands random thread delays and crashes. On average, it is 4.6x faster than lock-free Naive-dynamic PageRank (Alg. NDLF).

关键词： parallel PageRank algorithm Dynamic Frontier approach Lock-free algorithm

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High-Performance GraphBLAS Backend Prototype for NEC SX-Aurora TSUBASA 36

High-Performance GraphBLAS Backend Prototype for NEC SX-Auro...

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36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Afanasyev, Ilya Komatsu, Kazuhiko Lichmanov, Dmitry Voevodin, Vadim Kobayashi, Hiroaki Lomonosov Moscow State Univ Res Comp Ctr Moscow Russia Tohoku Univ Cybersci Ctr Sendai Miyagi Japan Tohoku Univ GSIS Sendai Miyagi Japan

ISBN: (纸本)9781665497473

Developing efficient graph algorithm implementations is a significant important problem of modern computer science since graphs are frequently used in various real-world applications. Graph algorithms typically belong to the data-intensive class, and thus using architectures with high-bandwidth memory potentially allows to solve many graph problems significantly faster than modern multicore CPUs. Among other supercomputer architectures, SX-Aurora TSUBASA vector engines are equipped with high-bandwidth memory, and thus, potentially allow to accelerate various graph applications. However, very few existing graph processing frameworks can efficiently utilize SX-Aurora hardware capabilities. GraphBLAS standard proposes a convenient way to develop graph algorithms in terms of linear algebra and allows its users not todeeply understand vector architectures hardware features. This paper describes a world-first attempt to implement an optimized prototype of the GraphBLAS backend for SX-Aurora TSUBASA. Our backend prototype can achieve performance, comparable to the existing Vector Graph Library (VGL) based implementations for SX-Aurora TSUBASA, and also outperform the existing GraphBLAS backends for NVIDIA GPUs. We also discuss the roadmap and challenges for creating the full GraphBLAS implementation for SX-Aurora TSUBASA vector engines.

关键词： Graph Algorithms Vector computing HPC GraphBLAS VGL Performance Optimizations Shortest Paths Page Rank SX-Aurora TSUBASA

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Sampling Policies for Near-Optimal Device Choice in parallel Simulations on CPU/GPU Platforms 28

Sampling Policies for Near-Optimal Device Choice in Parallel...

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28th IEEE/ACM international symposium on distributed Simulation and Real Time Applications, DS-RT 2024

作者： Andelfinger, Philipp Pellegrini, Alessandro Marotta, Romolo University of Rostock Germany Tor Vergata University of Rome Italy

ISBN: (纸本)9798331527211

Heterogeneous hardware platforms comprised of CPUs, GPUs, and other accelerators offer the opportunity to choose the best-suited device for executing a given scientific simulation in order to minimize execution time and energy consumption. To this end, the recently proposed "Follow the Leader"approach dynamically selects a suitable device based on runtime performance measurements during speculative discrete-event simulations. A currently active "leader"device is periodically challenged by a "follower"device in order to negotiate the new leader. The optimality of the device choices and the associated overhead depends critically on the challenge frequency and timing. Here, we explore policies to schedule challenges with the goal of attaining Pareto-optimal combinations of execution time and energy consumption. Several heuristics are first evaluated in an abstract fashion using a "meta-simulation"by mimicking the progress and energy consumption of an idealized co-execution. In this setting, we optimize the heuristics' tuning parameters to assess their relative merits in near-optimal configurations when compared to challenge timings based on perfect knowledge. We find that under challenging stochastic workloads based on a class of mean-reverting random walks, the best heuristics can closely approximate the execution time and energy consumption achievable under an optimal device choice. Empirical support for this observation is given by measurements of a CPU/GPU co-execution of the Time Warp algorithm on physical hardware. © 2024 IEEE.

关键词： Stochastic systems

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An MPI-parallel Algorithm for Static and Dynamic Top-k Harmonic Centrality 34

An MPI-Parallel Algorithm for Static and Dynamic Top-k Harmo...

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34th IEEE international symposium on Computer Architecture and High Performance computing (SBAC-PAD)

作者： van der Grinten, Alexander Custers, Geert Duy Le Thanh Meyerhenke, Henning Humboldt Univ Berlin Germany Delft Univ Technol Delft Netherlands

ISBN: (数字)9781665451550

ISBN: (纸本)9781665451550

Analyzing large graphs in parallel has received considerable attention recently due to ever increasing data set sizes. Centrality measures indicate the importance of vertices (or edges) and belong to the most widely used analytic kernels. Harmonic centrality is a popular vertex centrality measure with many desirable properties. Since most of the applications of vertex centrality rely only on the ranking of vertices and not on their exact centrality scores, previous research has considered various algorithms that can quickly determine a ranking of the top-k vertices with highest harmonic centrality. Such algorithms are available for many types of real-world graphs, including dynamic graphs. Yet, no attempts have been made to efficiently parallelize these top-k algorithms (besides naive implementations based on a global lock). In this paper, we propose an MPI-distributed algorithm for (dynamic) top-k harmonic centrality. Our algorithm exploits an algebraic BFS technique and batching to parallelize the approximation of centrality scores of multiple vertices. Likewise, we use algebraic techniques to compute various bounds and heuristics that are necessary to obtain a fast top-k algorithm. Experiments demonstrate that our MPI-parallel algorithm outperforms existing implementations. Consequently, our new approach allows the computation of top-k harmonic centrality on graphs that are substantially larger.

关键词： algebraic graph algorithm network analysis harmonic centrality top-k ranking MPI parallelism

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Practical Efficient Microservice Autoscaling with QoS Assurance 22

Practical Efficient Microservice Autoscaling with QoS Assura...

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31st international symposium on High-Performance parallel and distributed computing (HPDC)

作者： Hossen, Md Rajib Islam, Mohammad A. Ahmed, Kishwar Univ Texas Arlington Arlington TX 76019 USA Univ South Carolina Beaufort Bluffton SC USA

ISBN: (纸本)9781450391993

Cloud applications are increasingly moving away from monolithic services to agile microservices-based deployments. However, efficient resource management for microservices poses a significant hurdle due to the sheer number of loosely coupled and interacting components. The interdependencies between various microservices make existing cloud resource autoscaling techniques ineffective. Meanwhile, machine learning (ML) based approaches that try to capture the complex relationships in microservices require extensive training data and cause intentional SLO violations. Moreover, these ML-heavy approaches are slow in adapting to dynamically changing microservice operating environments. In this paper, we propose PEMA (Practical EfficientMicroservice Autoscaling), a lightweight microservice resource manager that finds efficient resource allocation through opportunistic resource reduction. PEMA's lightweight design enables novel workload-aware and adaptive resource management. Using three prototype microservice implementations, we show that PEMA can find efficient resource allocation and save up to 33% resource compared to the commercial rule-based resource allocations.

关键词： Autoscaling microservices resource management cloud computing quality of service

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SCANNS: Towards Scalable and Concurrent Data Indexing and Searching in High-End computing System 22

SCANNS: Towards Scalable and Concurrent Data Indexing and Se...

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22nd IEEE/ACM international symposium on Cluster, Cloud and Internet computing (CCGrid)

作者： Orhean, Alexandru Iulian Giannakou, Anna Ramakrishnan, Lavanya Chard, Kyle Raicu, Ioan IIT Coll Comp Chicago IL 60616 USA Lawrence Berkeley Natl Lab Sci Data Div Berkeley CA USA Univ Chicago Dept Comp Sci Chicago IL 60637 USA

ISBN: (纸本)9781665499569

Increasing data volumes, particularly in science and engineering, has resulted in the widespread adoption of parallel and distributed file systems for data storage and access. However, as file system sizes and the amount of data "owned" by users has grown, it is increasingly difficult to discover and locate data amongst the terabytes or petabytes of accessible data. While it is now routine to search for data on a personal computer or discover data online at the click of a button, there is no such equivalent method for discovering data on large parallel and distributed file systems in high-performance computing systems. Popular search solutions, such as Apache Lucene, were designed and implemented to run on commodity hardware thus posing significant limitations in achieving good efficiency on large-scale storage systems with many-core architectures, multiple NUMA nodes, and multiple NVMe storage devices. In this work we revisit and propose methods and techniques to support efficient indexing of data in order to enable search. We propose SCANNS, an indexing framework that can exploit the properties of modern high-performance computing systems delivering an order of magnitude better performance. SCANNS supports out-of-the-box Term Frequency-Inverse Document Frequency information retrieval model. We evaluate SCANNS on the Mystic system with configurations up to 192-cores, 768GiB of RAM, 8 NUMA nodes, and up to 16 NVMe drives, and achieved performance improvements up to 19x better indexing while delivering up to 280X lower search latency when compared to Apache Lucene.

关键词： search engine architecture high-performance indexing high-performance storage scientific data

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Energy-efficient online resource provisioning for cloud-edge platforms via multi-armed bandits 34

Energy-efficient online resource provisioning for cloud-edge...

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34th IEEE international symposium on Computer Architecture and High Performance computing (SBAC-PAD)

作者： Rey-Jouanchicot, Jordan del Castillo, Juan Angel Lorenzo Zuckerman, Stephane Veronica Belmega, E. CY Cergy Paris Univ CY Tech Cergy France CY Cergy Paris Univ ETIS UMR 8051 ENSEA CNRS F-95000 Cergy France Univ Gustave Eiffel CNRS LIGM F-77454 Marne La Vallee France

ISBN: (数字)9781665451574

ISBN: (纸本)9781665451574

Edge computing is a new paradigm in which data are locally collected, aggregated and preprocessed before being sent to a Cloud platform. Edge devices, typically IoT objects, are characterized by limited computational capabilities, alongside high energy efficiency operations. In smart building or smart city applications, the overall amount of available IoT devices may account for an important computational capacity. Aggregating the idle CPU cycles of several devices would allow performing on-site parallel and distributed computing, while reusing the available resources in the network. Timely and important computational tasks such as video surveillance processing, HPC jobs, or deep neural network training, can be performed with local available resources. This includes local servers or a private, on-site cloud, as opposed to the use of public clouds, thus avoiding security issues and fostering energy efficiency. An ongoing research topic in such a distributed and volatile context is to design novel algorithms for resource provisioning that allow tasks to be distributed over a set of available IoT devices in an efficient manner. This work presents two online, adaptive and robust scheduling techniques, based on the UCB and EXP3 Multi-Armed Bandit algorithms (MABs), for resource management in Cloud-Edge computing based environments while improving performance and reducing energy consumption. The novelty of our adapted algorithms lies in the fact that we explicitly account for unavailable computing IoT devices resulting in a time-varying available set of arms at each stage. Our numerical results show the high relevance of our approach in reaching optimal provisioning policies in time-varying environments.

关键词： Cloud Edge computing resource provisioning online learning multi-armed bandits

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parallel Accelerating Ultra-Long Read Alignment by Vertical Partitioning Data 13

Parallel Accelerating Ultra-Long Read Alignment by Vertical ...

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13th IEEE international symposium on parallel Architectures, Algorithms and Programming, PAAP 2022

作者： Pan, Deng Zhong, Cheng Chen, Danyang Zhang, Jinxiong Yang, Feng Guangxi University Key Laboratory of Parallel Distributed Computing Technology Guangxi Universities School of Computer Electronics and Information Guangxi Nanning China Guangxi University Key Laboratory of Parallel Distributed Computing School of Computer Electronics and Information Guangxi Nanning China

ISBN: (纸本)9781665452182

The alignment between sequencing reads and genome is a basic work in biological big data analysis. Each read of the third generation sequencing data is getting longer, and the data size is getting larger. To effectively solve the ultra-long read alignment problem with high requirements for computing and memory capacity, a strategy for vertical partitioning ultra-long reads on hybrid CPU/GPU cluster is proposed, and a heap data structure is used to filter the local aligned results in all computing nodes of the parallel cluster system according to the alignment score to reduce the data transmission size. The methods for early termination and parallel merging-splicing are used to accelerate splicing local aligned results. The local aligned results among all computing nodes are collected and extended to obtain the final alignment results. The experimental results on datasets of simulated and real ultra-long reads show that the proposed parallel alignment algorithm can obtain high alignment accuracy, sensitivity and base-level sensitivity as a whole, and accelerate completing alignment between ultra-long reads and reference genome. © 2022 IEEE.

关键词： Alignment

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