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FGCache: Accelerating Aggregation Queries for OLAP applications via Caching 29

FGCache: Accelerating Aggregation Queries for OLAP Applicati...

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29th ieee international Conference on parallel and distributed Systems, ICPADS 2023

作者： Wang, Can Jin, Peiquan Luo, Yongping Wang, Xiaoliang Wan, Shouhong University of Science and Technology of China Hefei China

ISBN: (纸本)9798350330717

OLAP applications often perform aggregation queries, such as sum, avg, count, max, and min, to process a large dataset for getting a small result, and the key challenge for optimizing aggregation queries is to reduce the data volume needed to be read from disks. To address this problem, this paper presents an efficient caching component called FGCache (Fine-Grained Cache) for aggregation queries. FGCache is a query-driven caching policy, which aims to maintain the results of aggregation queries in the memory according to the predicates of user queries. The query results are divided into sets with fine-grained querying intervals, which are maintained via an in-memory tree index. We experimentally evaluate the performance of FGCache on various datasets and settings. The results suggest the efficiency and effectiveness of our proposal. © 2023 ieee.

关键词： Aggregation query Cache OLAP

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Scalable dual-instruction multiple-data processing on an efficient systolic-array architecture

Scalable dual-instruction multiple-data processing on an eff...

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

作者： Tan, Yuxi Ben Abdelhamid, Riadh Guo, BingJie Gao, Qixiang Nishimura, Masaru Yamaguchi, Yoshiki Univ Tsukuba Grad Sch Sci & Technol 1-1-1 Ten Nou Dai Tsukuba Ibaraki 3058573 Japan Heidelberg Univ Inst Tech Informat Neuenheimer Feld 368 D-69120 Heidelberg Germany Univ Tsukuba Inst Syst & Informat Engn 1-1-1 Ten Nou Dai Tsukuba Ibaraki 3058573 Japan Kumamoto Univ Semicond & Digital Res & Educ Inst Chuo Ku 2-39-1 Kurokami Kumamoto Kumamoto 8608555 Japan

ISBN: (纸本)9798350364613;9798350364606

For half a century, advancements in semiconductor manufacturing and processor design have propelled the evolution of computer systems, yet power consumption remains a critical challenge for future innovation. Essential for sustainable development and performance gains in AI, big data, and autonomous driving, energy-efficient computing now demands a shift from boosting processing element (PE) performance through frequency increases to improving computational efficiency. This shift has prompted a move from Single- Instruction Multiple-Data (SIMD) to Single-Instruction Multiple- Thread (SIMT) architectures, like GPU s, offering computational speed and energy savings [1]-[3]. Despite SIMD's effectiveness, its limitations necessitate further architectural innovations, such as predicate bits and advanced thread management in GPU s. Our research introduces a dual-instruction multiple-data (DIMD) architecture to enhance PE parallelism efficiently for FPGA overlay architecture. U sing the Fast Fourier Transform (FFT) [4], [5] and AI convolution [6] as benchmarks, we demonstrate the proposed architecture's potential for effectively handling complex parallel computations. © 2024 ieee.

关键词： FPGA Many-core architecture Overlay SIMD

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Improving Cryptanalytic applications with Stochastic Runtimes on GPUs

Improving Cryptanalytic Applications with Stochastic Runtime...

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35th ieee international parallel and distributed processing symposium (IPDPS)

作者： Oden, Lena Keller, Joerg Fernuniv Fac Math & Comp Sci Hagen Germany

ISBN: (纸本)9781665435772

We investigate cryptanalytic applications comprised of many independent tasks that exhibit a stochastic runtime distribution. We compare four algorithms for executing such applications on GPUs. We demonstrate that for different distributions, problem sizes, and platforms the best strategy varies. We support our analytic results by extensive experiments on two different GPUs, from different sides of the performance spectrum: A high performance GPU (Nvidia Volta) and an energy saving system on chip (Jetson Nano).

关键词： Cryptanalytic applications Stochastic Runtime Graphics processing Units Performance Evaluation

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An Asynchronous Dataflow-Driven Execution Model For distributed Accelerator Computing 23

An Asynchronous Dataflow-Driven Execution Model For Distribu...

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23rd ieee/ACM international symposium on Cluster, Cloud and Internet Computing (CCGrid)

作者： Salzmann, Philip Knorr, Fabian Thoman, Peter Gschwandtner, Philipp Cosenza, Biagio Fahringer, Thomas Univ Innsbruck Distributed & Parallel Syst Grp Innsbruck Austria Univ Salerno Salerno Italy

ISBN: (纸本)9798350301199

While domain-specific HPC software packages continue to thrive and are vital to many scientific communities, a general purpose high-productivity GPU cluster programming model that facilitates experimentation for non-experts remains elusive. We demonstrate how Celerity, a high-level C++ programming model for distributed accelerator computing based on the open SYCL standard, allows for the quick development of - and experimentation with - distributed applications. To achieve scalability on large machines, we replace Celerity's existing master/worker scheduling model with a fully distributed scheme that reduces the worst-case scheduling complexity from quadratic to linear while maintaining the existing programming interface. We then show how this declarative, data-flow based API paired with a point-to-point communication model with eager data pushing can effectively expose and leverage opportunities for latency hiding and computation/communication overlapping with minimal or no manual guidance. We demonstrate how Celerity exhibits very good scalability on multiple benchmarks from several scientific domains and up to 128 GPUs.

关键词： Accelerator Computing GPGPU Cluster Computing Runtime System SYCL

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RIMR: Reverse Influence Maximization Rank

RIMR: Reverse Influence Maximization Rank

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

作者： Vap, Jay Kogge, Peter Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA

ISBN: (纸本)9798350364613;9798350364606

The ubiquitous usage of social media and other internet platforms has led to the widespread proliferation of naturally occurring social networks. This has caused a revolution in marketing and information dissemination due to the rise of influencers, and their innate ability to broadcast information to large audiences. Influence maximization, a key problem in identifying optimal influential individuals, can be used to identify influencers in a social network, or can be superimposed on other settings which exhibit relational information. Popular methods, such as greedy algorithms, face scalability issues, performing poorly on larger networks, and incurring a large memory overhead. To address these challenges, this paper introduces a novel methodology, RIMR, which leverages statistical modeling and parallel programming to circumvent the need for storing large collections of data, achieving scalability and efficiency. Experimental results demonstrate the effectiveness and scalability of the proposed approach compared to state-of-the-art methods, showcasing significant speedups. This paper provides insights into the development of the proposed statistical process, the heterogeneous algorithm implementation, and a prototype demonstrating its efficacy.

关键词： GPUs Heterogeneous Architectures Influence Maximization parallel Algorithms

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Coupling streaming AI and HPC ensembles to achieve 100-1000x faster biomolecular simulations 36

Coupling streaming AI and HPC ensembles to achieve 100-1000x...

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36th ieee international parallel and distributed processing symposium (ieee IPDPS)

作者： Brace, Alexander Yakushin, Igor Ma, Heng Trifan, Anda Munson, Todd Foster, Ian Ramanathan, Arvind Lee, Hyungro Turilli, Matteo Jha, Shantenu Argonne Natl Lab Data Sci & Learning Div Lemont IL 60439 USA Argonne Natl Lab Math & Comp Sci Div Lemont IL USA Univ Chicago Dept Comp Sci Chicago IL 60637 USA Univ Illinois Ctr Biophys Champaign IL USA Rutgers State Univ RAD Lab New Brunswick NJ USA Brookhaven Natl Lab Computat Sci Initiat Upton NY 11973 USA

ISBN: (纸本)9781665481069

Machine learning (ML)-based steering can improve the performance of ensemble-based simulations by allowing for online selection of more scientifically meaningful computations. We present DeepDriveMD, a framework for ML-driven steering of scientific simulations that we have used to achieve orders-ofmagnitude improvements in molecular dynamics (MD) performance via effective coupling of ML and HPC on large parallel computers. We discuss the design of DeepDriveMD and characterize its performance. We demonstrate that DeepDriveMD can achieve between 100-1000x acceleration for protein folding simulations relative to other methods, as measured by the amount of simulated time performed, while covering the same conformational landscape as quantified by the states sampled during a simulation. Experiments are performed on leadershipclass platforms on up to 1020 nodes. The results establish DeepDriveMD as a high-performance framework for ML-driven HPC simulation scenarios, that supports diverse MD simulation and ML back-ends, and which enables new scientific insights by improving the length and time scales accessible with current computing capacity.

关键词： Proteins Couplings Computers Accelerometers Solid modeling distributed processing Computational modeling

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9th ieee international Workshop on Automatic Performance Tuning (iWAPT 2024)

9th IEEE International Workshop on Automatic Performance Tun...

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ieee international symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： Valero Lara Pedro

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

iWAPT (international Workshop on Automatic Performance Tuning) is a series of workshops that focus on research and techniques related to performance sustainability issues. The series provides an opportunity for researchers and users of automatic performance tuning (AT) technologies to exchange ideas and experiences acquired when applying such technologies to improve the performance of algorithms, libraries, and applications; in particular, on cutting edge computing platforms. Topics of interest include performance modeling; adaptive algorithms; autotuned numerical algorithms; libraries and scientific applications; empirical compilation; automated code generation; frameworks and theories of AT and software optimization; autonomic computing; and context-aware computing.

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Optimal Arbitrary Pattern Formation on a Grid by Asynchronous Autonomous Robots 36

Optimal Arbitrary Pattern Formation on a Grid by Asynchronou...

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36th ieee international parallel and distributed processing symposium (ieee IPDPS)

作者： Hector, Rory Sharma, Gokarna Vaidyanathan, Ramachandran Trahan, Jerry L. Louisiana State Univ Baton Rouge LA 70803 USA Kent State Univ Kent OH 44242 USA

ISBN: (纸本)9781665481069

We consider the distributed setting of N autonomous mobile robots that operate in Look-Compute-Move (LCM) cycles following either the robots with lights model or the classical oblivious robots model. For the lights model, we assume obstructed visibility so that a robot cannot see another robot if a third robot is positioned between them on the straight line connecting them. In contrast, we assume unobstructed visibility in the classical model so that a robot sees all others irrespective of their positions. In addition, we consider a grid-based terrain embedded in the 2-dimensional Euclidean plane that restricts each robot's movement to one of the four neighboring grid points from its current position. This grid setting is a natural discretization of the 2-dimensional real plane and extends the robot swarm model in directions of greater applicability. The ARBITRARY PATTERN FORMATION problem is to relocate the N robots (starting at arbitrary but distinct initial positions on a grid) to form an arbitrary target pattern given as input. In this paper, we provide two asynchronous algorithms for ARBITRARY PATTERN FORMATION, one on the lights model and another on the classical model. Key measures of the algorithms' performance include the time taken and the number of moves by each robot. Both algorithms run in O(max{D-i, D-p}) time with O(max{D-i, D-p}) moves by each robot, where D-i and D-p, respectively, are the diameters of the initial and pattern configurations. The algorithm for the lights model uses O(1) colors. We also prove a lower bound of Omega(max{D-i, D-p}) for time for any ARBITRARY PATTERN FORMATION algorithm if scaling is not allowed on the target pattern. Therefore, our algorithms are optimal w.r.t. time. Furthermore, our algorithms are also optimal w.r.t. the number of moves given the existing lower bound of Omega(max{D-i, D-p}) on the number of moves. In sum, our results show that having lights provides a trade-off on the unobstructed visibility requiremen

关键词： Pattern formation distributed processing Color Time measurement Mobile robots Robots Autonomous robots

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As easy as ABC: Optimal (A)ccountable (B)yzantine (C)onsensus is easy! 36

As easy as ABC: Optimal (A)ccountable (B)yzantine (C)onsensu...

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36th ieee international parallel and distributed processing symposium (ieee IPDPS)

作者： Civit, Pierre Gilbert, Seth Gramoli, Vincent Guerraoui, Rachid Komatovic, Jovan Sorbonne Univ LIP6 CNRS Paris France NUS Singapore Singapore Singapore Univ Sydney EPFL Sydney NSW Australia Ecole Polytech Fed Lausanne EPFL Lausanne Switzerland

ISBN: (纸本)9781665481069

It is known that the agreement property of the Byzantine consensus problem among n processes can be violated in a non-synchronous system if the number of faulty processes exceeds t(0) = left perpendicularn/3right perpendicular - 1 [10], [19]. In this paper, we investigate the accountable Byzantine consensus problem in non-synchronous systems: the problem of solving Byzantine consensus whenever possible (e.g., when the number of faulty processes does not exceed t(0)) and allowing correct processes to obtain proof of culpability of (at least) t(0) + 1 faulty processes whenever correct processes disagree. We present four complementary contributions: 1) We introduce ABC: a simple yet efficient transformation of any Byzantine consensus protocol to an accountable one. ABC introduces an overhead of only two all-toall communication rounds and O(n(2)) additional bits in executions with up to t(0) faults (i.e., in the common case). 2) We define the accountability complexity, a complexity metric representing the number of accountabilityspecific messages that correct processes must send. Furthermore, we prove a tight lower bound. In particular, we show that any accountable Byzantine consensus protocol incurs cubic accountability complexity. Moreover, we illustrate that the bound is tight by applying the ABC transformation to any Byzantine consensus protocol. 3) We demonstrate that, when applied to an optimal Byzantine consensus protocol, ABC constructs an accountable Byzantine consensus protocol that is (1) optimal with respect to the communication complexity in solving consensus whenever consensus is solvable, and (2) optimal with respect to the accountability complexity in obtaining accountability whenever disagreement occurs. 4) We generalize ABC to other distributed computing problems besides the classic consensus problem. We characterize a class of agreement tasks, including reliable and consistent broadcast [5], that ABC renders accountable.

关键词： Measurement distributed processing Lattices Complexity theory Consensus protocol Reliability Task analysis

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MICCO: An Enhanced Multi-GPU Scheduling Framework for Many-Body Correlation Functions 36

MICCO: An Enhanced Multi-GPU Scheduling Framework for Many-B...

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36th ieee international parallel and distributed processing symposium (ieee IPDPS)

作者： Wang, Qihan Ren, Bin Chen, Jie Edwards, Robert G. William & Mary Dept Comp Sci Williamsburg VA 23185 USA Jefferson Lab Newport News VA USA

ISBN: (纸本)9781665481069

Calculation of many-body correlation functions is one of the critical kernels utilized in many scientific computing areas, especially in Lattice Quantum Chromodynamics (Lattice QCD). It is formalized as a sum of a large number of contraction terms each of which can be represented by a graph consisting of vertices describing quarks inside a hadron node and edges designating quark propagations at specific time intervals. Due to its computation- and memory-intensive nature, real-world physics systems (e.g., multi-meson or multi-baryon systems) explored by Lattice QCD prefer to leverage multi-GPUs. Different from general graph processing, many-body correlation function calculations show two specific features: a large number of computation/data-intensive kernels and frequently repeated appearances of original and intermediate data. The former results in expensive memory operations such as tensor movements and evictions. The latter offers data reuse opportunities to mitigate the dataintensive nature of many-body correlation function calculations. However, existing graph-based multi-GPU schedulers cannot capture these data-centric features, thus resulting in a sub-optimal performance for many-body correlation function calculations. To address this issue, this paper presents a multi-GPU scheduling framework, MICCO, to accelerate contractions for correlation functions particularly by taking the data dimension (e.g., data reuse and data eviction) into account. This work first performs a comprehensive study on the interplay of data reuse and load balance, and designs two new concepts: local reuse pattern and reuse bound to study the opportunity of achieving the optimal trade-off between them. Based on this study, MICCO proposes a heuristic scheduling algorithm and a machine-learning-based regression model to generate the optimal setting of reuse bounds. Specifically, MICCO is integrated into a real-world Lattice QCD system, Redstar, for the first time running on multiple GPUs.

关键词： distributed processing Correlation Tensors Quantum computing Scientific computing Scheduling algorithms Lattices

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