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Vectron: A Dynamic programming Auto-vectorization Framework 25

Vectron: A Dynamic Programming Auto-vectorization Framework

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23rd ACM/IEEE international symposium on Code Generation and Optimization, CGO 2025

作者： Moghaddasi, Sourena Naser Smajlović, Haris Shajii, Ariya Numanagić, Ibrahim University of Victoria Victoria Canada Exaloop Boston United States

ISBN: (纸本)9798400712753

Dynamic programming (DP) is a fundamental algorithmic strategy that decomposes large problems into manageable subproblems. It is a cornerstone of many important computational methods in diverse fields, especially in the field of computational genomics, where it is used for sequence comparison. However, as the scale of the data keeps increasing, these algorithms are becoming a major computational bottleneck, and there is a need for strategies that can improve their performance. Here, we present Vectron, a novel auto-vectorization suite that targets array-based DP implementations written in Python and converts them to efficient vectorized counterparts that can efficiently process multiple problem instances in parallel. Leveraging Single Instruction Multiple Data (SIMD) capabilities in modern CPUs, along with Graphics Processing Units (GPUs), Vectron delivers significant speedups, ranging from 10% to more than 20×, over the conventional C++ implementations and manually vectorized and domain-specific state-of-the-art implementations, without necessitating large algorithm or code changes. Vectron’s generality enables automatic vectorization of any array-based DP algorithm and, as a result, presents an attractive solution to optimization challenges inherent to DP algorithms. © 2025 Copyright held by the owner/author(s).

关键词： Graphics processing unit

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8th international symposium on Combinatorial Optimization, ISCO 2024

8th International Symposium on Combinatorial Optimization, I...

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8th international symposium on Combinatorial Optimization, ISCO 2024

ISBN: (纸本)9783031609237

The proceedings contain 30 papers. The special focus in this conference is on Combinatorial Optimization. The topics include: Quadratically Constrained Reformulation, Strong Semidefinite programming Bounds, and algorithms for the Chordless Cycle Problem;a Family of Spanning-Tree Formulations for the Maximum Cut Problem;optimal Cycle Selections: An Experimental Assessment of Integer programming Formulations;1-Persistency of the Clique Relaxation of the Stable Set Polytope;alternating Direction Method and Deep Learning for Discrete Control with Storage;branch and Cut for Partitioning a Graph into a Cycle of Clusters;computing the Edge Expansion of a Graph Using Semidefinite programming;minimizing External Vertices in Hypergraph Orientations;open-Separating Dominating Codes in Graphs;on the Complexity of the Minimum Chromatic Violation Problem;crystal Trees;Reducing Treewidth for SAT-Related Problems Using Simple Liftings;total Matching and Subdeterminants;a New Structural Parameter on Single Machine Scheduling with Release Dates and Deadlines;fixed-Parameter algorithms for Cardinality-Constrained Graph Partitioning Problems on Sparse Graphs;sequencing Stochastic Jobs with a Single Sample;the Thief Orienteering Problem on Series-parallel Graphs;approximation Algorithm for Job Scheduling with Reconfigurable Resources;network Design on Undirected Series-parallel Graphs;online Graph Coloring with Predictions;neuron Pairs in Binarized Neural Networks Robustness Verification via Integer Linear programming;optimal Counterfactual Explanations for k-Nearest Neighbors Using Mathematical Optimization and Constraint programming;surrogate Constraints for Synchronized Energy Production/Consumption;a Robust Two-Stage Model for the Urban Air Mobility Flight Scheduling Problem;optimal Charging Station Location in a Linear Cycle Path with Deviations;an Efficient Timing Algorithm for Drivers with Rest Periods;fair Energy Allocation for Collective Self-consumption;day-Ahead Lot-Sizing Un

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parallel Approximations for High-Dimensional Multivariate Normal Probability Computation in Confidence Region Detection Applications 38

Parallel Approximations for High-Dimensional Multivariate No...

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international parallel and Distributed Processing symposium (IPDPS)

作者： Zhang, Xiran Abdulah, Sameh Cao, Jian Ltaief, Hatem Sun, Ying Genton, Marc G. Keyes, David E. King Abdullah Univ Sci & Technol Comp Elect & Math Sci & Engn Div Thuwal Saudi Arabia King Abdullah Univ Sci & Technol Stat Program Thuwal Saudi Arabia King Abdullah Univ Sci & Technol Extreme Comp Res Ctr Thuwal 23955 Saudi Arabia Univ Houston Dept Math Houston TX USA

ISBN: (纸本)9798350387117;9798350387124

Addressing the statistical challenge of computing the multivariate normal (MVN) probability in high dimensions holds significant potential for enhancing various applications. For example, the critical task of detecting confidence regions where a process probability surpasses a specific threshold is essential in diverse applications, such as pinpointing tumor locations in magnetic resonance imaging (MRI) scan images, determining hydraulic parameters in groundwater flow issues, and forecasting regional wind power to optimize wind turbine placement, among numerous others. One common way to compute high-dimensional MVN probabilities is the Separation-of-Variables (SOV) algorithm. This algorithm is known for its high computational complexity of O(n(3)) and space complexity of O(n(2)), mainly due to a Cholesky factorization operation for an n x n covariance matrix, where n represents the dimensionality of the MVN problem. This work proposes a high-performance computing framework that allows scaling the SOV algorithm and, subsequently, the confidence region detection algorithm. The framework leverages parallel linear algebra algorithms with a task-based programming model to achieve performance scalability in computing process probabilities, especially on large-scale systems. In addition, we enhance our implementation by incorporating Tile Low-Rank (TLR) approximation techniques to reduce algorithmic complexity without compromising the necessary accuracy. To evaluate the performance and accuracy of our framework, we conduct assessments using simulated data and a wind speed dataset. Our proposed implementation effectively handles high-dimensional multivariate normal (MVN) probability computations on shared and distributed-memory systems using finite precision arithmetics and TLR approximation computation. Performance results show a significant speedup of up to 20X in solving the MVN problem using TLR approximation compared to the reference dense solution without sacrificing

关键词： Cholesky factorization Confidence region detection Excursion Set Multivariate normal probability Separation-of-Variables algorithm Tile low-rank

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Many Sequential Iterative algorithms Can Be parallel and (Nearly) Work-efficient 22

Many Sequential Iterative Algorithms Can Be Parallel and (Ne...

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34th ACM symposium on parallelism in algorithms and architectures (SPAA)

作者： Shen, Zheqi Wan, Zijin Gu, Yan Sun, Yihan UC Riverside Riverside CA 92521 USA

ISBN: (纸本)9781450391467

Some recent papers showed that many sequential iterative algorithms can be directly parallelized, by identifying the dependences between the input objects. This approach yields many simple and practical parallel algorithms, but there are still challenges to achieve work-efficiency and high-parallelism. Work-efficiency means that the number of operations is asymptotically the same as the best sequential solution. This can be hard for certain problems where the number of dependences between objects is asymptotically more than optimal sequential work, and we cannot even afford the cost to generate them. To achieve high-parallelism, we always want it to process as many objects as possible in parallel. The goal is to achieve (O) over tilde (D) span for a problem with the deepest dependence length D. We refer to this property as round-efficiency. This paper presents work-efficient and round-efficient algorithms for a variety of classic problems and propose general approaches to do so. To efficiently parallelize many sequential iterative algorithms, we propose the phase-parallel framework. The framework assigns a rank to each object and processes the objects based on the order of their ranks. All objects with the same rank can be processed in parallel. To enable work-efficiency and high parallelism, we use two types of general techniques. Type 1 algorithms aim to use range queries to extract all objects with the same rank to avoid evaluating all the dependences. We discuss activity selection, and Dijkstra's algorithm using Type 1 framework. Type 2 algorithms aim to wake up an object when the last object it depends on is finished. We discuss activity selection, longest increasing subsequence (LIS), greedy maximal independent set (MIS), and many other algorithms using Type 2 framework. All of our algorithms are (nearly) work-efficient and round-efficient, and some of them (e.g., LIS) are the first to achieve the both. Many of them improve the previous best bounds. Moreover,

关键词： parallel algorithms phase-parallel framework parallel programming sequential iterative algorithms activity selection longest increasing subsequence maximal independent set independence system

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Proceedings - 2022 IEEE 36th international parallel and Distributed Processing symposium Workshops, IPDPSW 2022

Proceedings - 2022 IEEE 36th International Parallel and Dist...

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36th IEEE international parallel and Distributed Processing symposium Workshops, IPDPSW 2022

ISBN: (纸本)9781665497473

The proceedings contain 148 papers. The topics discussed include: heterogeneous architecture for sparse data processing;combined application of approximate computing techniques in DNN hardware accelerators;highly efficient ALLTOALL and ALLTOALLV communication algorithms for GPU systems;implementing spatio-temporal graph convolutional networks on graphcore IPUs;the best of many worlds: scheduling machine learning inference on CPU-GPU integrated architectures;online learning RTL synthesis for automated design space exploration;machine learning aided hardware resource estimation for FPGA DNN implementations;optimal schedules for high-level programming environments on FPGAs with constraint programming;on how to push efficient medical semantic segmentation to the edge: the SENECA approach;and exploiting high-bandwidth memory for FPGA-acceleration of inference on sum-product networks.

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An Evolvable Swarm-parallel Algorithm Framework for Satellite-Ground Networking Problem 3

An Evolvable Swarm-Parallel Algorithm Framework for Satellit...

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3rd international symposium on Aerospace Engineering and Systems, ISAES 2024

作者： Chen, Cheng Du, Yonghao Yao, Feng College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (纸本)9798350350418

Satellite Internet has been deployed rapidly, leading to an explosive growth in satellite scale, further strengthening the contradiction between the growing demand for satellite-ground communication and the limited ground station resources. To address this challenge, this paper introduces an evolvable swarm-parallel algorithm framework (ESPAF), which comprises a swarm-parallel solving and deconflicting framework, a learning-assisted metaheuristic (LM) fast-solving algorithm that combines reinforcement learning (RL) and Tabu simulated annealing (TSA), and a linear programming (LP) exact-solving algorithm. The ESPAF breaks through the traditional task-driven satellite scheduling approach found in existing research and presents a novel and appropriate method for optimizing satellite-ground networking under a new macro paradigm. A series of experimental instances involving up to 1,000 satellites and 100 ground stations demonstrate the efficient performance of ESPAF. Comparative experiments with alternative metaheuristic algorithms and the CPLEX solver further emphasized ESP AF's ability to generate high-quality solutions more rapidly. © 2024 IEEE.

关键词： Simulated annealing

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CuGraph C++ primitives: Vertex/edge-centric building blocks for parallel graph computing

CuGraph C++ primitives: Vertex/edge-centric building blocks ...

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2023 IEEE international parallel and Distributed Processing symposium Workshops, IPDPSW 2023

作者： Kang, Seunghwa Hastings, Chuck Eaton, Joe Rees, Brad NVIDIA United States

ISBN: (纸本)9798350311990

Software development of high-performance graph algorithms is difficult on modern parallel computers. To simplify this task, we have designed and implemented a collection of C++ graph primitives, basic building blocks, within cuGraph to assist graph analytics software developers on parallel computers, ranging from desktops to large clusters. This graph primitives API provides a vertex/edge-centric C++ Standard Template Library (STL)-like interface, allowing users to pick a primitive algorithm, and specify desired operations on vertices and edges and how to reduce the output of such operations through C++ functors. The API implementation is responsible for executing these functors on the underlying hardware. In this case, the graph primitives are implemented to run on NVIDIA GPU systems, from a single-GPU to multi-GPUs in a distributed cluster. RAPIDS cuGraph is NVIDIA's graph analytics solution for data scientists and software integrators. algorithms in cuGraph are either implemented using the cuGraph C++ primitives API or being migrated over to using the primitives API. The Louvain and PageRank algorithms have been tested on clusters with over 1000 GPUs. © 2023 IEEE.

关键词： C++ (programming language)

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Sunstone: A Scalable and Versatile Scheduler for Mapping Tensor Algebra on Spatial Accelerators

Sunstone: A Scalable and Versatile Scheduler for Mapping Ten...

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IEEE international symposium on Performance Analysis of Systems and Software (ISPASS)

作者： Olyaiy, MohammadHossein Ng, Christopher Fedorova, Alexandra (Sasha) Lis, Mieszko Univ British Columbia Vancouver BC Canada

ISBN: (纸本)9798350397390

Tensor algebra, the main component of several popular machine learning techniques, benefits from modern accelerators due to the massive parallelism and data reuse available. To achieve the benefits, however, optimizing the dataflow is crucial: prior works showed that 19x energy savings are possible by tuning the dataflow. This optimization is challenging because: (1) the optimization space for modern chip architectures with several levels of memory and multiple levels of spatial processing is vast, and (2) distinct tensor computations follow different memory access and reuse patterns. In this manuscript, we algebraically analyze the possible reuse when executing tensor workloads on an accelerator. Based on our analysis, we develop several principles that significantly reduce the dataflow optimization space even for modem, complex chip architectures. Moreover, these principles are transferable to various tensor workloads with different memory access patterns. Compared to prior work, our techniques can find dataflow for typical tensor workloads up to 800x faster and with up to 1.9x better energy-delay products.

关键词： dataflow computing accelerator architectures scheduling algorithms neural network hardware parallel processing

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Implementation of the QGD Algorithm Using AMR Technology and GPU parallel Computing 24th

Implementation of the QGD Algorithm Using AMR Technology and...

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24th international Conference on Computational Science (ICCS)

作者： But, Ivan Epikhin, Andrey Kirushina, Maria Elizarova, Tatiana RAS Ivannikov Inst Syst Programming Moscow 109004 Russia RAS Keldysh Inst Appl Math Moscow 125047 Russia

ISBN: (纸本)9783031637858;9783031637834

The paper presents an algorithm based on the quasi-gasdynamic approach for the solution of unsteady compressible flows over a wide range of Mach numbers. It is implemented on the AMReX open platform, which uses adaptive mesh refinement technology to facilitate parallelization of computations on GPU architectures. To validate its effectiveness, the developed solver is applied to the numerical simulation of the shock-vortex interaction problem with flow parameter values of M-v = 0.9 and M-s = 1.5. Cross-validation to assess its performance is conducted with OpenFOAM-based solvers, specifically rhoCentralFoam and QGDFoam. Schlieren fields are used to evaluate oscillations of the numerical schemes and algorithms, while resolution capabilities of the algorithm are assessed by comparing density fields in five cross-sections with the reference values.

关键词： Shock-vortex interactions Compressible flow Quasi-gas dynamic equations OpenFOAM AMReX

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Toward Self-Adjusting k-Ary Search Tree Networks

Toward Self-Adjusting k-Ary Search Tree Networks

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

作者： Feder, Evgeniy Paramonov, Anton Mavrin, Pavel Salem, Iosif Schmid, Stefan Aksenov, Vitaly ITMO Univ St Petersburg Russia Ecole Polytech Fed Lausanne Lausanne Switzerland Neapolis Univ Pafos Pafos Cyprus TU Berlin Berlin Germany City Univ London London England

ISBN: (纸本)9798350364613;9798350364606

Datacenter networks are becoming increasingly flexible with the incorporation of new optical communication technologies, such as optical circuit switches, enabling self-adjusting topologies that can adapt to the traffic pattern in a demand-aware manner. In this paper, we take the first steps toward demand-aware and self-adjusting k-ary tree networks. These are more powerful generalizations of existing binary search tree networks (like SplayNet [14]), which have been at the core of self-adjusting network (SAN) designs. k-ary search tree networks are a natural generalization offering nodes of higher degrees, reduced route lengths, and local routing in spite of reconfigurations (due to maintaining the search property). Our main results are algorithms for static k-ary tree networks and two online heuristics for self-adjusting k-ary tree networks.

关键词： self-adjusting networks k-ary trees online algorithms dynamic programming

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