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Creation of a CS1 Course with Modern C++ principles 2024

Creation of a CS1 Course with Modern C++ Principles

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55th acm Technical symposium on Computer Science Education, SIGCSE 2024

作者： Dougherty, Ryan E. United States Military Academy West PointNY United States

ISBN: (纸本)9798400704246

Best practices in programming need to be emphasized in a CS1 course as bad student habits persist if not reinforced well. The C++ programming language, although a relatively old language, has been regularly updated with new versions since 2011, on the pace of once every three years. Each new version contains important features that make the C++ language more complex for backwards compatibility, but often introduce new features to make common use cases simpler to implement. This poster contains experiences in designing a CS1 course that uses the C++ programming language that incorporates "modern"versions of the language from the start, as well as recent conferences about the language. Our goals were to prevent many common bad habits among C++ programmers. © 2024 Owner/Author.

关键词： C++ (programming language)

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Sustainable Supercomputing for AI: GPU Power Capping at HPC Scale 14

Sustainable Supercomputing for AI: GPU Power Capping at HPC ...

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14th Annual acm symposium on Cloud computing (SoCC)

作者： Zhao, Dan Samsi, Siddharth McDonald, Joseph Li, Baolin Bestor, David Jones, Michael Tiwari, Devesh Gadepally, Vijay MIT Cambridge MA 02139 USA MIT Lincoln Lab Cambridge MA USA Northeastern Univ Boston MA USA

ISBN: (纸本)9798400703874

As research and deployment of AI grows, the computational burden to support and sustain its progress inevitably does too. To train or fine-tune state-of-the-art models in NLP, computer vision, etc., some form of AI hardware acceleration is virtually a requirement. Recent large language models require considerable resources to train and deploy, resulting in significant energy usage, potential carbon emissions, and massive demand for GPUs and other hardware accelerators. However, this surge carries large implications for energy sustainability at the HPC/datacenter level. In this paper, we study the effects of power-capping GPUs at a research supercomputing center on GPU temperature and power draw;we show significant decreases in both temperature and power draw, reducing power consumption and potentially improving hardware life-span, with minimal impact on job performance. To our knowledge, our work is the first to conduct and make available a detailed analysis of the effects of GPU power-capping at the supercomputing scale. We hope our work will inspire HPCs/datacenters to further explore, evaluate, and communicate the impact of power-capping AI hardware accelerators for more sustainable AI.

关键词： artificial intelligence deep learning distributed systems sustainable computing high-performance computing

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PODC 2013 - proceedings of the 2013 acm symposium on principles of distributed computing

PODC 2013 - Proceedings of the 2013 ACM Symposium on Princip...

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2013 acm symposium on principles of distributed computing, PODC 2013

ISBN: (纸本)9781450320658

The proceedings contain 54 papers. The topics discussed include: distributed computing theory for wireless networks and mobile systems;pragmatic primitives for non-blocking data structures;the SkipTrie: low-depth concurrent search without rebalancing;optimal deterministic routing and sorting on the congested clique;Byzantine vector consensus in complete graphs;fast Byzantine agreement in dynamic networks;synchronous Byzantine agreement with nearly a cubic number of communication bits;how to meet asynchronously at polynomial cost;on the complexity of universal leader election;on the complexity of universal leader election;on minimum interaction time for continuous distributed interactive computing;scalable anonymous communication with Byzantine adversary;brokerage and closure in a strategic model of social capital;and feedback from nature: an optimal distributed algorithm for maximal independent set selection.

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Online Dependency-aware Task offloading in Cloudlet-based Edge computing Networks 21

Online Dependency-aware Task offloading in Cloudlet-based Ed...

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21st acm International symposium on Mobility Management and Wireless Access (MobiWac)

作者： Oskoui, Mohammad Reza Golzari Sanso, Brunilde Ecole Polytech Montreal Montreal PQ Canada

ISBN: (纸本)9798400703676

The demand for low-latency processing in mobile devices is dramatically increasing. However, mobile devices inherently lack the capacity to handle heavy and low-latency processing. Edge computing techniques, which offload the tasks of user applications to a nearby server, are being used to mitigate this problem. Moreover, applications requiring rapid processing have evolved, becoming more complex. Application tasks are no longer merely computational and independent;each task requires its associated libraries and dependencies, all of which must be considered during offloading. The dependency between tasks should also be taken into account. Centralized decision-making for offloading for a large number of users is not practically feasible. In response to this issue, we designed a distributed method based on deep reinforcement learning. We defined the states of the learning agent in a manner that enables users to learn about the environment with respect to the level of task dependency. Through simulation, we demonstrate that the proposed algorithm surpasses existing benchmarks in terms of application completion time by identifying the optimal server for offloading dependent tasks.

关键词： Edge computing Internet of Things Offloading Reinforcement learning

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Gallatin: A General-Purpose GPU Memory Manager 24

Gallatin: A General-Purpose GPU Memory Manager

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29th acm SIGPLAN Annual symposium on principles and Practice of Parallel Programming (PPoPP)

作者： McCoy, Hunter Pandey, Prashant Univ Utah Salt Lake City UT 84112 USA

ISBN: (纸本)9798400704352

Dynamic memory management is critical for efficiently porting modern data processing pipelines to GPUs. However, building a general-purpose dynamic memory manager on GPUs is challenging due to the massive parallelism and weak memory coherence. Existing state-of-the-art GPU memory managers, Ouroboros and Reg-Eff, employ traditional data structures such as arrays and linked lists to manage memory objects. They build specialized pipelines to achieve performance for a fixed set of allocation sizes and fall back to the CUDA allocator for allocating large sizes. In the process, they lose general-purpose usability and fail to support critical applications such as streaming graph processing. In this paper, we introduce Gallatin, a general-purpose and high-performance GPU memory manager. Gallatin uses the van Emde Boas (vEB) tree data structure to manage memory objects efficiently and supports allocations of any size. Furthermore, we develop a highly-concurrentGPUimplementation of the vEB tree which can be broadly used in other GPU applications. It supports constant time insertions, deletions, and successor operations for a given memory size. In our evaluation, we compare Gallatin with state-of-theart specialized allocator variants. Gallatin is up to 374x faster on single-sized allocations and up to 264xfaster on mixed-size allocations than the next-best allocator. In scalability benchmarks, Gallatin is up to 254x times faster than the next-best allocator as the number of threads increases. For the graph benchmarks, Gallatin is 1.5x faster than the state-of-the-art for bulk insertions, slightly faster for bulk deletions, and is 3x faster than the next-best allocator for all graph expansion tests.

关键词： GPU Memory allocation Concurrent data structures High performance computing

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Accelerating Function-Centric Applications by Discovering, Distributing, and Retaining Reusable Context in Workflow Systems 24

Accelerating Function-Centric Applications by Discovering, D...

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33rd International symposium on High-Performance Parallel and distributed computing (HPDC)

作者： Thanh Son Phung Thomas, Colin Ward, Logan Chard, Kyle Thain, Douglas Univ Notre Dame Notre Dame IN 46556 USA Argonne Natl Lab Lemont IL USA Univ Chicago Chicago IL USA

ISBN: (纸本)9798400704130

Workflow systems provide a convenient way for users to write large-scale applications by composing independent tasks into large graphs that can be executed concurrently on high-performance clusters. In many newer workflow systems, tasks are often expressed as a combination of function invocations in a high-level language. Because necessary code and data are not statically known prior to execution, they must be moved into the cluster at runtime. An obvious way of doing this is to translate function invocations into self-contained executable programs and run them as usual, but this brings a hefty performance penalty: a function invocation now needs to piggyback its context with extra code and data to a remote node, and the remote node needs to take extra time to reconstruct the invocation's context before executing it, both detrimental to lightweight short-running functions. A better solution for workflow systems is to treat functions and invocations as first-class abstractions: subsequent invocations of the same function on a worker node should only pay for the cost of context setup once and reuse the context between different invocations. The remaining problems lie in discovering, distributing, and retaining the reusable context among workers. In this paper, we discuss the rationale and design requirement of these mechanisms to support context reuse, and implement them in TaskVine, a data-intensive distributed framework and execution engine. Our results from executing a large-scale neural network inference application and a molecular design application show that treating functions and invocations as first-class abstractions reduces the execution time of the applications by 94.5% and 26.9%, respectively.

关键词： Workflow Systems Serverless computing distributed Storage Burst Buffers

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FAAST: An Efficient Serverless Framework Made Snapshot-based Function Response Fast 24

FAAST: An Efficient Serverless Framework Made Snapshot-based...

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33rd International symposium on High-Performance Parallel and distributed computing (HPDC)

作者： Bai, Yongshu Yang, Zhihui Gao, Fen Zhejiang Lab Hangzhou Zhejiang Peoples R China Zhejiang Univ Hangzhou High Tech Zone Binjiang Inst Blockchain & Data Secur Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9798400704130

Recent years have seen the rapid popularity of serverless computing, benefiting from its seamless scalability and pay-as-you-go charging model. Despite recent efforts to enhance function cold-start performance by utilizing snapshot and working set mechanisms, the existing methods still suffered considerable overheads in end-to-end response time as functions' inputs varied. In this paper, we propose FAAST, a novel solution designed to improve function response time by enhancing virtual machine snapshot-based startup while guaranteeing function execution performance. Our system introduces a preparation phase to generate a lightweight working set, which excludes stale pages that do not require loading from snapshot files for each function to expedite startup. Additionally, FAAST facilitates network connectivity for the snapshot by implementing NAT within the virtio-net device model bypassing the time-consuming network namespace setup. To guarantee execution performance, a divergent page fault handling mechanism is designed to address page faults related to stale and non-stale pages separately, effectively mitigating sluggish disk I/O accesses. FAAST also incrementally adapts the working set file across various function invocations to bolster functions' resilience to diverse inputs. Extensive experiments demonstrate that FAAST reduces function end-to-end time by up to 63% compared to the state-of-the-art.

关键词： cloud computing serverless computing virtualization snapshot working set

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Daedalus: Self-Adaptive Horizontal Autoscaling for Resource Efficiency of distributed Stream Processing Systems 24

Daedalus: Self-Adaptive Horizontal Autoscaling for Resource ...

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15th acm/SPEC International Conference on Performance Engineering (ICPE)

作者： Pfister, Benjamin J. J. Scheinert, Dominik Geldenhuys, Morgan K. Kao, Odej Tech Univ Berlin Berlin Germany

ISBN: (纸本)9798400704444

distributed Stream Processing (DSP) systems are capable of processing large streams of unbounded data, offering high throughput and low latencies. To maintain a stable Quality of Service (QoS), these systems require a sufficient allocation of resources. At the same time, over-provisioning can result in wasted energy and high operating costs. Therefore, to maximize resource utilization, autoscaling methods have been proposed that aim to efficiently match the resource allocation with the incoming workload. However, determining when and by how much to scale remains a significant challenge. Given the long-running nature of DSP jobs, scaling actions need to be executed at runtime, and to maintain a good QoS, they should be both accurate and infrequent. To address the challenges of autoscaling, the concept of self-adaptive systems is particularly fitting. These systems monitor themselves and their environment, adapting to changes with minimal need for expert involvement. This paper introduces Daedalus, a self-adaptive manager for autoscaling in DSP systems, which draws on the principles of self-adaption to address the challenge of efficient autoscaling. Daedalus monitors a running DSP job and builds performance models, aiming to predict the maximum processing capacity at different scaleouts. When combined with time series forecasting to predict future workloads, Daedalus proactively scales DSP jobs, optimizing for maximum throughput and minimizing both latencies and resource usage. We conducted experiments using Apache Flink and Kafka Streams to evaluate the performance of Daedalus against two state-of-the-art approaches. Daedalus was able to achieve comparable latencies while reducing resource usage by up to 71%.

关键词： distributed Stream Processing Autoscaling System Tuning Performance Modeling Resource Management Cloud computing

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BigDataflow: A distributed Interprocedural Dataflow Analysis Framework 2023

BigDataflow: A Distributed Interprocedural Dataflow Analysis...

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31st acm Joint Meeting of the European Software Engineering Conference / symposium on the Foundations-of-Software-Engineering (ESEC/FSE)

作者： Sun, Zewen Xu, Duanchen Zhang, Yiyu Qi, Yun Wang, Yueyang Zuo, Zhiqiang Wang, Zhaokang Li, Yue Li, Xuandong Lu, Qingda Peng, Wenwen Guo, Shengjian Nanjing Univ Nanjing Peoples R China Alibaba Grp Sunnyvale CA USA Baidu Res Sunnyvale CA USA Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9798400703270

Apart from forming the backbone of compiler optimization, static dataflow analysis has been widely applied in a vast variety of applications, such as bug detection, privacy analysis, program comprehension, etc. Despite its importance, performing interprocedural dataflow analysis on large-scale programs is well known to be challenging. In this paper, we propose a novel distributed analysis framework supporting the general interprocedural dataflow analysis. Inspired by large-scale graph processing, we devise a dedicated distributed worklist algorithm tailored for interprocedural dataflow analysis. We implement the algorithm and develop a distributed framework called BigDataflow running on a large-scale cluster. The experimental results validate the promising performance of BigDataflow - it can finish analyzing the program of millions lines of code in minutes. Compared with the state-of-the-art, BigDataflow achieves much more analysis efficiency.

关键词： interprocedural dataflow analysis distributed computing

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Oblivious Paxos: Privacy-Preserving Consensus Over Secret-Shares 14

Oblivious Paxos: Privacy-Preserving Consensus Over Secret-Sh...

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14th Annual acm symposium on Cloud computing (SoCC)

作者： Kurnia, Fadhil I. Venkataramani, Arun Univ Massachusetts Amherst MA 01003 USA

ISBN: (纸本)9798400703874

State-of-the-art consensus protocols like Paxos reveal the values being agreed upon to all nodes, but some deployment scenarios involving a subset of nodes outsourced to public cloud providers motivate hiding the value. In this work, we present the primary-backup secret-shared state machine (PBSSM) architecture and an underlying consensus protocol Oblivious Paxos (OPaxos) that enable strong consistency, high availability, privacy, and fast common-case performance. OPaxos enables privacy-preserving consensus by allowing acceptors to safely agree on a secret-shared value without utrusted acceptors knowing the value. We also present Fast Oblivious Paxos (Fast-OPaxos), which enables consensus over secret-shares in three one-way delays under low concurrency settings. Our prototype-driven microbenchmarks and smarthome case study show that OPaxos induces a negligible latency overhead of at most 0.1 ms compared to Paxos while maintaining more than 85% of Paxos' capacity for small requests, and can provide lower latency and higher capacity compared to Paxos for large request sizes.

关键词： distributed Consensus Privacy Secret-Sharing

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