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Scheduling Jobs with Work-Inefficient parallel Solutions 24

Scheduling Jobs with Work-Inefficient Parallel Solutions

引用

36th acm symposium on parallelism in algorithms and architectures (SPAA)

作者： Kuszmaul, William Westover, Alek Harvard Univ Cambridge MA 02138 USA MIT Cambridge MA USA

ISBN: (纸本)9798400704161

This paper introduces the serial-parallel decision problem. Consider an online scheduler that receives a series of tasks, where each task has both a parallel and a serial implementation. The parallel implementation has the advantage that it can make progress concurrently on multiple processors, but the disadvantage that it is (potentially) work-inefficient. As tasks arrive, the scheduler must decide for each task which implementation to use. We begin by studying total awake time. We give a simple decide-on-arrival scheduler that achieves a competitive ratio of 3 for total awake time-this scheduler makes serial/parallel decisions immediately when jobs arrive. Our second result is an parallel-work-oblivious scheduler that achieves a competitive ratio of 6 for total awake time-this scheduler makes all of its decisions based only on the size of each serial job and without needing to know anything about the parallel implementations. Finally, we prove a lower bound showing that, if a scheduler wishes to achieve a competitive ratio of O(1), it can have at most one of the two aforementioned properties (decide-on-arrival or parallel-work-oblivious). We also prove lower bounds of the form 1 + Omega(1) on the optimal competitive ratio for any scheduler. Next, we turn our attention to optimizing mean response time. Here, we show that it is possible to achieve an O(1) competitive ratio with O(1) speed augmentation. This is the most technically involved of our results. We also prove that, in this setting, it is not possible for a parallel-work-oblivious scheduler to do well. In addition to these results, we present tight bounds on the optimal competitive ratio if we allow for arrival dependencies between tasks (e.g., tasks are components of a single parallel program), and we give an in-depth discussion of the remaining open questions.

关键词： Scheduling parallel Work-Inefficient Competitive-Analysis

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Massively parallel algorithms for Approximate Shortest Paths 24

Massively Parallel Algorithms for Approximate Shortest Paths

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36th acm symposium on parallelism in algorithms and architectures (SPAA)

作者： Dory, Michal Matar, Shaked Univ Haifa Haifa Israel Ben Gurion Univ Negev Beer Sheva Israel

ISBN: (纸本)9798400704161

We present fast algorithms for approximate shortest paths in the massively parallel computation (MPC) model. We provide randomized algorithms that take poly(log log n) rounds in the near-linear memory MPC model. Our results are for unweighted undirected graphs with.. vertices and m edges. Our first contribution is a (1 + epsilon)-approximation algorithm for Single-Source Shortest Paths (SSSP) that takes poly(log log n) rounds in the near-linear MPC model, where the memory per machine is (O) over tilde (n) and the total memory is (O) over tilde (mn(rho)), where rho is a small constant. Our second contribution is a distance oracle that allows to approximate the distance between any pair of vertices. The distance oracle is constructed in poly(log log n) rounds and allows to query a (1+epsilon) (2k-1)-approximate distance between any pair of vertices u and v in O(1) additional rounds. The algorithm is for the near-linear memory MPC model with total memory of size (O) over tilde ((m+n(1+rho))n(1/k)), where rho is a small constant. While our algorithms are for the near-linear MPC model, in fact they only use one machine with (O) over tilde (n) memory, where the rest of machines can have sublinear memory of size (O) over tilde (n(gamma)) for a small constant gamma < 1. All previous algorithms for approximate shortest paths in the near-linear MPC model either required Omega(log n) rounds or had an Omega(log n) approximation. Our approach is based on fast construction of near-additive emulators, limited-scale hopsets and limited-scale distance sketches that are tailored for the MPC model. While our end-results are for the near-linear MPC model, many of the tools we construct such as hopsets and emulators are constructed in the more restricted sublinear MPC model.

关键词： Approximate Shortest Paths Massively parallel Computation Hopsets Emulators Distance Oracles

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Efficient parallel Reinforcement Learning Framework Using the Reactor Model 24

Efficient Parallel Reinforcement Learning Framework Using th...

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36th acm symposium on parallelism in algorithms and architectures (SPAA)

作者： Kwok, Jacky Lohstroh, Marten Lee, Edward A. Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9798400704161

parallel Reinforcement Learning (RL) frameworks are essential for mapping RL workloads to multiple computational resources, allowing for faster generation of samples, estimation of values, and policy improvement. These computational paradigms require a seamless integration of training, serving, and simulation workloads. Existing frameworks, such as Ray, are not managing this orchestration efficiently, especially in RL tasks that demand intensive input/output and synchronization between actors on a single node. In this study, we have proposed a solution implementing the reactor model, which enforces a set of actors to have a fixed communication pattern. This allows the scheduler to eliminate work needed for synchronization, such as acquiring and releasing locks for each actor or sending and processing coordination-related messages. Our framework, Lingua Franca (LF), a coordination language based on the reactor model, also supports true parallelism in Python and provides a unified interface that allows users to automatically generate dataflow graphs for RL tasks. In comparison to Ray on a single-node multi-core compute platform, LF achieves 1.21x and 11.62x higher simulation throughput in OpenAI Gym and Atari environments, reduces the average training time of synchronized parallel Q-learning by 31.2%, and accelerates multi-agent RL inference by 5.12x.

关键词： parallel Computing Reinforcement Learning Programming Languages Machine Learning Model of Computation

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A Simpler and parallelizable O(√log n)-approximation Algorithm for SPARSEST CUT 24

A Simpler and Parallelizable O(√log n)-approximation Algori...

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36th acm symposium on parallelism in algorithms and architectures (SPAA)

作者： Kolmogorov, Vladimir Inst Sci & Technol Austria ISTA Klosterneuburg Austria

ISBN: (纸本)9798400704161

Currently, the best known tradeoff between approximation ratio and complexity for the Sparsest Cut problem is achieved by the algorithm in [Sherman, FOCS 2009]: it computes O(root(log n)/epsilon)-approximation using O(n(epsilon) log(O(1))n) maxflows for any epsilon is an element of[Theta(1/log n), Theta(1)]. It works by solving the SDP relaxation of [Arora-Rao-Vazirani, STOC 2004] using the Multiplicative Weights Update algorithm (MW) of [Arora-Kale, Jacm 2016]. To implement one MW step, Sherman approximately solves a multicommodity flow problem using another application of MW. Nested MW steps are solved via a certain "chaining" algorithm that combines results of multiple calls to the maxflow algorithm. We present an alternative approach that avoids solving the multicommodity flow problem and instead computes "violating paths". This simplifies Sherman's algorithm by removing a need for a nested application of MW, and also allows parallelization: we show how to compute O(root(log n)/epsilon)-approximation via O(log(O(1)) n) maxflows using O(n(epsilon)) processors. We also revisit Sherman's chaining algorithm, and present a simpler version together with a new analysis.

关键词： SPARSEST CUT approximation algorithms parallel algorithms

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Fault-Tolerant parallel Integer Multiplication 24

Fault-Tolerant Parallel Integer Multiplication

引用

36th acm symposium on parallelism in algorithms and architectures (SPAA)

作者： Nissim, Roy Schwartz, Oded Spiizer, Yuval Hebrew Univ Jerusalem Dept Comp Sci Jerusalem Israel

ISBN: (纸本)9798400704161

Exascale machines have a small mean time between failures, necessitating fault tolerance. Out-of-the-box fault-tolerant solutions, such as checkpoint-restart and replication, apply to any algorithm but incur significant overhead costs. Long integer multiplication is a fundamental kernel in numerical linear algebra and cryptography. The naive, schoolbook multiplication algorithm runs in Theta(n(2)) while Toom-Cook algorithms runs in Theta(n(logk (2k -1)) for 2 <= k. We obtain the first efficient fault-tolerant parallel Toom-Cook algorithm. While asymptotically faster FFT-based algorithms exist, Toom-Cook algorithms are often favored in practice on small scale and on supercomputers. Our algorithm enables fault tolerance with negligible overhead costs. Compared to existing, general-purpose, fault-tolerant solutions, our algorithm reduces the arithmetic and communication (bandwidth) overhead costs by a factor of Theta(P/(2k -1)) (where P is the number of processors). To this end, we adapt the fault-tolerant BFS-DFS method of Birnbaum et al. (2020) for fast matrix multiplication and combine it with a coding strategy tailored for Toom-Cook. This eliminates the need for recomputations, resulting in a much faster algorithm.

关键词： Fault Tolerance Long Integer Multiplication Toom-Cook parallel Computing I/O Complexity

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SPAA 2024 - proceedings of the 36th acm symposium on parallelism in algorithms and architectures

SPAA 2024 - Proceedings of the 36th ACM Symposium on Paralle...

引用

36th acm symposium on parallelism in algorithms and architectures, SPAA 2024

ISBN: (纸本)9798400704161

The proceedings contain 54 papers. The topics discussed include: expediting hazard pointers with bounded RCU critical sections;Alock: asymmetric lock primitive for RDMA systems;when is parallelism fearless and zero-cost with rust?;efficient parallel reinforcement learning framework using the reactor model;parallel best arm identification in heterogeneous environments;brief announcement: lock-free learned search data structure;brief announcement: LIT: lookup interlocked table for range queries;brief announcement: a fast scalable detectable unrolled lock-based linked list;scheduling out-trees online to optimize maximum flow;optimizing dynamic data center provisioning through speed scaling: a primal-dual perspective;scheduling jobs with work-inefficient parallel solutions;and multi bucket queues: efficient concurrent priority scheduling.

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Brief Announcement: Work Stealing through Partial Asynchronous Delegation 24

Brief Announcement: Work Stealing through Partial Asynchrono...

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36th acm symposium on parallelism in algorithms and architectures (SPAA)

作者： Wang, Jiawei Liu, Yutao Fu, Ming Haertig, Hermann Chen, Haibo Tech Univ Dresden Huawei Dresden Res Ctr Dresden Germany Huawei Dresden Res Ctr Dresden Germany Huawei Cent Software Inst Shenzhen Peoples R China Tech Univ Dresden Dresden Germany Shanghai Jiao Tong Univ Huawei Cent Software Inst Shanghai Peoples R China

ISBN: (纸本)9798400704161

Work stealing is a well-established technique in multi-core systems that aims to improve load balancing and task scheduling efficiency. Each processing unit maintains its own task queue, and when idle, it steals tasks from other units. Traditional work-stealing approaches face performance bottlenecks due to costly synchronization primitives and contention arising from concurrent access by both the queue owner and thieves. The state-of-the-art solution addresses these issues through coarse-grained synchronization;however, it restricts stealing in specific scenarios, thereby limiting parallelism. We introduce PadWS, a partial and asynchronous delegated work-stealing algorithm. PadWS employs a block-based design in which, under common cases, the queue owner and thieves work on separate blocks, reducing metadata contention. Delegation is partially enabled for the block in which the owner is located, allowing thieves to steal from it-an approach that deviates from the current block-based approach. Additionally, our delegation strategy is asynchronous, which removes the need for thieves to spin-wait after sending a request.

关键词： parallel processing scheduling work stealing delegation

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The Dataflow Abstract Machine Simulator Framework 51

The Dataflow Abstract Machine Simulator Framework

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acm/IEEE 51st annual International symposium on Computer Architecture (ISCA)

作者： Zhang, Nathan Lacouture, Rubens Sohn, Gina Mure, Paul Zhang, Qizheng Kjolstad, Fredrik Olukotun, Kunle Stanford Univ Stanford CA 94305 USA MIT 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9798350326598;9798350326581

The growing interest in novel dataflow architectures and streaming execution paradigms has created the need for a simulator optimized for modeling dataflow systems. To fill this need, we present three new techniques that make it feasible to simulate complex systems consisting of thousands of components. First, we introduce an interface based on Communicating Sequential Processes which allows users to simultaneously describe functional and timing characteristics. second, we introduce a scalable point-to-point synchronization scheme that avoids global synchronization. Finally, we demonstrate a technique to exploit slack in the simulated system, such as FIFOs, to increase simulation parallelism. We implement these techniques in the Dataflow Abstract Machine (DAM), a parallel simulator framework for dataflow systems. We demonstrate the benefits of using DAM by highlighting three case studies using the framework. First, we use DAM directly as an exploration tool for streaming algorithms on dataflow hardware. We simulate two different implementations of the attention algorithm used in large language models, and use DAM to show that the second implementation only requires a constant amount of local memory. second, we re-implement a simulator for a sparse tensor algebra accelerator, resulting in 57% less code and a simulation speedup of up to four orders of magnitude. Finally, we demonstrate a general technique for time-multiplexing real hardware to simulate multiple virtual copies of the hardware using DAM.

关键词： Dataflow Accelerators Modeling parallel Discrete Event Simulation

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Brief Announcement: Red-Blue Pebbling with Multiple Processors: Time, Communication and Memory Trade-offs 24

Brief Announcement: Red-Blue Pebbling with Multiple Processo...

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36th acm symposium on parallelism in algorithms and architectures (SPAA)

作者： Boehnlein, Toni Papp, Pal Andras Yzelman, Albert-Jan N. Huawei Zurich Res Ctr Comp Syst Lab Zurich Switzerland

ISBN: (纸本)9798400704161

The well-studied red-blue pebble game models the execution of an arbitrary computational DAG by a single processor over a two-level memory hierarchy. We present a natural generalization to a multiprocessor setting where each processor has its own limited fast memory, and all processors share unlimited slow memory. To our knowledge, this is the first thorough study that combines pebbling and DAG scheduling problems, capturing the computation of general workloads on multiple processors with memory constraints and communication costs. Our pebbling model enables us to analyze trade-offs between workload balancing, communication and memory limitations, and it captures real-world factors such as superlinear speedups due to parallelization. Our results include upper and lower bounds on the pebbling cost, an analysis of a greedy pebbling strategy, and an extension of NP-hardness results for specific DAG classes from simpler models. For our main technical contribution, we show two inapproximability results that already hold for the long-standing problem of standard red-blue pebbling: (i) the optimal I/O cost cannot be approximated to any finite factor, and (ii) the optimal total cost (I/O+computation) can only be approximated to a limited constant factor, i.e., it does not allow for a polynomial-time approximation scheme. These results also carry over naturally to our multiprocessor pebbling model.

关键词： Red-blue pebble game Limited memory Scheduling parallel computing Approximation Communication costs

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Brief Announcement: A parallel Architecture for Dynamic Approximate Membership 23

Brief Announcement: A Parallel Architecture for Dynamic Appr...

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35th acm symposium on parallelism in algorithms and architectures (SPAA)

作者： Even, Guy Domingues, Gabriel Marques Toutian, Parham Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9781450395458

We present the first parallel architecture for a dynamic approximate membership data-structure (i.e., a filter) that supports insertions, deletions, and approximate membership queries. Our architecture borrows techniques from PRAM emulation to obtain a parallel filter based on two levels of fingerprint-dictionaries. A key component in the architecture is a special-purpose wide-word processor we designed to support operations over small dictionaries. We implemented this architecture on an FPGA running at 100MHz. The implementation stores up to 1.44 million keys, has a false-positive rate less than 0.3%, receives batches 16 of operations per cycle, preserves sequential order, and runs with a stable throughput of over a billion operations per second with respect to several benchmarks.

关键词： data structures Bloom filter PRAM

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