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Understanding VASP Power Profiles on NVIDIA A100 GPUs

Understanding VASP Power Profiles on NVIDIA A100 GPUs

2024 Workshops of the International Conference for High Performance computing, Networking, Storage and analysis, SC Workshops 2024

作者： Zhao, Zhengji Austin, Brian Rrapaj, Ermal Wright, Nicholas J. Lawrence Berkeley National Laboratory Berkeley United States

ISBN: (纸本)9798350355543

Power is a critical limiting factor in supercomputing as systems scale to exascale levels. To advance scientific computing, supercomputers must operate efficiently under limited power budgets. Power-aware scheduling can help by enforcing power management strategies, but this requires a deep understanding of application power behavior, especially on modern GPU-centric supercomputers. This study examines the power behavior of VASP, a leading HPC application, on the Perlmutter A100 GPU system at NERSC. We explore how VASP's power usage changes with various inputs and parallelism, and assess its response to power cappings. We find that VASP's power usage varies significantly with different workloads, more so than with parallel concurrency. Additionally, power capping GPUs to 50% of their Thermal Design Power can be applied to most VASP workloads with less than a 10% performance loss. These findings shed light on the feasibility and effectiveness of power-aware scheduling based on application power profiles on HPC systems. © 2024 IEEE.

关键词： A100 GPU application power profile computing center performance power capping VASP

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PEACH: Proactive and Environment-Aware Channel State Information Prediction with Depth Images

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proceedings of the acm on measurement and analysis of computing systems 2023年第1期7卷 1-27页

作者： Ayvasik, Serkut Mehmeti, Fidan Babaians, Edwin Kellerer, Wolfgang Tech Univ Munich Chair Commun Networks Munich Germany Tech Univ Munich Chair Media Technol Munich Germany

Up-to-date and accurate prediction of Channel State Information (CSI) is of paramount importance in UltraReliable Low-Latency Communications (URLLC), specifically in dynamic environments where unpredictable mobility is inherent. CSI can be meticulously tracked by means of frequent pilot transmissions, which on the downside lead to an increase in metadata (overhead signaling) and latency, which are both detrimental for URLLC. To overcome these issues, in this paper, we take a fundamentally different approach and propose PEACH, a machine learning system which utilizes environmental information with depth images to predict CSI amplitude in beyond 5G systems, without requiring metadata radio resources, such as pilot overheads or any feedback mechanism. PEACH exploits depth images by employing a convolutional neural network to predict the current and the next 100 ms CSI amplitudes. The proposed system is experimentally validated with extensive measurements conducted in an indoor environment, involving two static receivers and two transmitters, one of which is placed on top of a mobile robot. We prove that environmental information can be instrumental towards proactive CSI amplitude acquisition of both static and mobile users on base stations, while providing an almost similar performance as pilot-based methods, and completely avoiding the dependency on feedback and pilot transmission for both downlink and uplink CSI information. Furthermore, compared to demodulation reference signal based traditional pilot estimation in ideal conditions without interference, our experimental results show that PEACH yields the same performance in terms of average bit error rate when channel conditions are poor (using low order modulation), while not being much worse when using higher modulation orders, like 16-QAM or 64-QAM. More importantly, in the realistic cases with interference taken into account, our experiments demonstrate considerable improvements introduced by PEACH in terms of n

关键词： 5G New Radio URLLC measurement Dataset Environment-Aware Wireless Communications Wireless Channel Estimation Channel State Information Proactive Prediction Convolutional Neural Network

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Lightweight Acquisition and Ranging of Flows in the Data Plane

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proceedings of the acm on measurement and analysis of computing systems 2023年第3期7卷 1-24页

作者： Monterubbiano, Andrea Langlet, Jonatan Walzer, Stefan Antichi, Gianni Reviriego, Pedro Pontarelli, Salvatore Univ Rome Sapienza Rome Italy Queen Mary Univ London London England Univ Cologne Cologne Germany Politecn Milan Milan Italy Univ Politecn Madrid Madrid Spain

As networks get more complex, the ability to track almost all the flows is becoming of paramount importance. This is because we can then detect transient events impacting only a subset of the traffic. Solutions for flow monitoring exist, but it is getting very difficult to produce accurate estimations for every tuple given the memory constraints of commodity programmable switches. Indeed, as networks grow in size, more flows have to be tracked, increasing the number of tuples to be recorded. At the same time, end-host virtualization requires more specific flowIDs, enlarging the memory cost for every single entry. Finally, the available memory resources have to be shared with other important functions as well (e.g., load balancing, forwarding, ACL). To address those issues, we present FlowLiDAR (Flow Lightweight Detection and Ranging), a new solution that is capable of tracking almost all the flows in the network while requiring only a modest amount of data plane memory which is not dependent on the size of flowIDs. We implemented the scheme in P4, tested it using real traffic from ISPs and compared it against four state-of-the-art solutions: FlowRadar, NZE, PR-sketch, and Elastic Sketch. While those can only reconstruct up to 60% of the tuples, FlowLiDAR can track 98.7% of them with the same amount of memory.

关键词： Programmable Data Plane High-Speed Networking Flow measurement

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A Quantitative analysis and Guidelines of Data Streaming Accelerator in Modern Intel^® Xeon^® Scalable Processors 24

A Quantitative Analysis and Guidelines of Data Streaming Acc...

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29th acm International Conference on Architectural Support for Programming Languages and Operating systems (ASPLOS)

作者： Kuper, Reese Jeong, Ipoom Yuan, Yifan Wang, Ren Ranganathan, Narayan Rao, Nikhil Hu, Jiayu Kumar, Sanjay Lantz, Philip Kim, Nam Sung Univ Illinois Chicago IL 60680 USA Intel Labs Hillsboro OR USA Tencent San Francisco CA USA

ISBN: (纸本)9798400703850

As semiconductor power density is no longer constant with the technology process scaling down, we need *** solutions if we are to continue scaling application performance. To this end, modern CPUs are integrating capable data accelerators on the chip, aiming to improve performance and *** for a wide range of applications and usages. One such accelerator is the Intel((R)) Data Streaming Accelerator (DSA) introduced since Intel((R)) 4th Generation Xeon((R)) Scalable CPUs (Sapphire Rapids). DSA targets data movement operations in memory that are common sources of overhead in datacenter workloads and infrastructure. In addition, it supports a wider range of operations on streaming data, such as CRC32 calculations, computation of deltas between data ***, and data *** (DIF) operations. This paper aims to introduce the latest features supported by DSA, dive deep into its versatility, and analyze its throughput *** through a comprehensive evaluation with both microbenchmarks and real use cases. Along with the analysis of its characteristics and the rich software ecosystem of DSA, we summarize several insights and guidelines for the programmer to make the most out of DSA, and use an in-depth case study of DPDK Vhost to demonstrate how these guidelines bene.t a real application.

关键词： Data streaming accelerator (DSA) accelerator measurement

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Batching of Tasks by Users of Pseudonymous Forums: Anonymity Compromise and Protection

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proceedings of the acm on measurement and analysis of computing systems 2023年第1期7卷 1-41页

作者： Goldberg, Alexander Fanti, Giulia Shah, Nihar B. Carnegie Mellon Univ Comp Sci Dept 5000 Forbes Ave Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Elect & Comp Engn 5000 Forbes Ave Pittsburgh PA 15213 USA Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA 15213 USA

There are a number of forums where people participate under pseudonyms. One example is peer review, where the identity of reviewers for any paper is confidential. When participating in these forums, people frequently engage in "batching": executing multiple related tasks (e.g., commenting on multiple papers) at nearly the same time. Our empirical analysis shows that batching is common in two applications we consider - peer review and Wikipedia edits. In this paper, we identify and address the risk of deanonymization arising from linking batched tasks. To protect against linkage attacks, we take the approach of adding delay to the posting time of batched tasks. We first show that under some natural assumptions, no delay mechanism can provide a meaningful differential privacy guarantee. We therefore propose a "one-sided" formulation of differential privacy for protecting against linkage attacks. We design a mechanism that adds zero-inflated uniform delay to events and show it can preserve privacy. We prove that this noise distribution is in fact optimal in minimizing expected delay among mechanisms adding independent noise to each event, thereby establishing the Pareto frontier of the trade-off between the expected delay for batched and unbatched events. Finally, we conduct a series of experiments on Wikipedia and Bitcoin data that corroborate the practical utility of our algorithm in obfuscating batching without introducing onerous delay to a system.

关键词： differential privacy peer review deanonymization attacks traffic analysis

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SEATED 2024 - proceedings of the 2024 Workshop on Serverless at the Edge, Part of: HPDC 2024 - 33rd International Symposium on High-Performance Parallel and Distributed computing

SEATED 2024 - Proceedings of the 2024 Workshop on Serverless...

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1st Workshop on Serverless at the Edge, SEATED 2024

ISBN: (纸本)9798400706479

The proceedings contain 3 papers. The topics discussed include: analysis and evaluation of load management strategies in a decentralized FaaS environment: a simulation-based framework;live migration of multi-container Kubernetes pods in multi-cluster serverless edge systems;and comparing actor-critic and neuroevolution approaches for traffic offloading in FaaS-powered edge systems.

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POMACS V8, N3, December 2024 Editorial

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proceedings of the acm on measurement and analysis of computing systems 2024年第3期8卷 1-1页

作者： Oh, Sewoong Sitaraman, Ramesh k. VAN Houdt, Benny Univ Washington Seattle WA 98195 USA Univ Massachusetts Amherst MA USA Akamai Technol Cambridge MA USA Univ Antwerp Antwerp Belgium

The proceedings of the acm on measurement and analysis of computing systems (POMACS) focuses on the measurement and performance evaluation of computer systems and operates in close collaboration with the acm Special Interest Group SIGMETRICS. All papers in this issue of POMACS will be presented at the acm SIGMETRICS 2025 conference on June 9-13, 2025, in Stony Brook, New York, *** papers in this issue were selected during the summer submission round by the 93 members of the acm SIGMETRICS 2025 program committee via a rigorous review process. Each paper was conditionally accepted (and shepherded), or allowed to be resubmitted to one of the subsequent two SIGMETRICS deadlines, or rejected (with resubmission allowed only after a year).POMACS is publishing 20 papers out of 110 submissions in this issue, of which 3 had previously received a one-shot revision decision. Submissions typically receive at least three reviews. While most papers were decided in the online discussion phase, borderline cases were extensively discussed during the online program committee meeting. Based on the indicated track(s), roughly 18% of the submissions were in the Theory track, 39% were in the measurement & Applied Modeling track, 27% were in the systems track, and 16% were in the Learning *** individuals contributed to the success of this issue of POMACS. We thank the authors, who submitted their best work to SIGMETRICS/POMACS. We would also like to thank the program committee members who provided constructive feedback in their reviews to authors and participated in the online discussions and program committee meeting. Further, we thank the several external reviewers who provided their expert opinions on specific submissions that required additional input. Finally, we are also grateful to the acm SIGMETRICS Chair, Mor Harchol-Balter, to the SIGMETRICS Organization Committee and to the SIGMETRICS Executive Committee for their ongoing efforts and initiatives for creating an exciti

关键词： computer systems measurement modeling performance analysis

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A measurement System for Distributed UWB-based Ranging and Localization in Snow Avalanches 23

A Measurement System for Distributed UWB-based Ranging and L...

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29th Annual International Conference on Mobile computing and Networking, MobiCom 2023

作者： Kuß, Jonas Köhler, Anselm Neuhauser, Michael Fischer, Jan-Thomas Neurauter, Rene Gerstmayr, Johannes Dressler, Falko Tu Berlin Berlin60011604 Germany Austrian Research Centre for Forests Innsbruck60026496 Austria University of Innsbruck Innsbruck60009999 Austria

ISBN: (纸本)9781450399906

We present a systems approach for tracking of mobile systems in an avalanche scenario using an ultra-wideband (UWB)-based ranging and localization system. UWB-based positioning is particularly challenging in outdoor scenarios covering large distances in complex topography. In the long-term, we are interested in tracking the motion of snow avalanches;particularly their inner dynamics are still unknown since they remain hidden for most observation approaches. Our system model considers multiple anchors distributed with inter-node distances in the order of a few hundred meters. Mobile nodes, which move with the avalanche in the field, are tracked via UWB time-of-flight measurements and further supported by inertial measurement units. Our system further integrates LoRa and IEEE 802.11 mesh for configuration and management, with UWB for measurement and reporting. First measurement results in the field show good accuracy and confirm the design choice. © 2023 acm.

关键词： Ultra-wideband (UWB)

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An Approach for Dynamic Behavioural Prediction and Fault Injection in Cyber-Physical systems 10

An Approach for Dynamic Behavioural Prediction and Fault Inj...

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16th IEEE/acm International Conference on Utility and Cloud computing (UCC) / 10th IEEE/acm International Conference on Big Data computing, Applications and Technologies (BDCAT)

作者： Adeyemo, Hayatullahi Bolaji Bahsoon, Rami Tino, Peter Univ Birmingham Birmingham W Midlands England Usmanu Danfodiyo Univ Gidan Alkali Nigeria

ISBN: (纸本)9798400704734

Modern technology integrates Cyber-Physical systems (CPS), merging computational and physical processes. Ensuring CPS dependability is vital in averting adverse effects on critical applications due to unforeseen behaviour. To fortify CPS resilience, a novel technique for dynamic behavioural prediction and fault injection is introduced. It predicts dynamic CPS behaviour through system modelling under diverse operational scenarios, employing a fault model with diverse fault classes. Unlike the single model tenet, this approach engages multiple expert models to simulate both faultless and faulty behaviours. By adopting this approach, we can inject specialised faults and scale the analysis of the faults together or separately. Injecting faults assesses system reactions and reveals vulnerabilities. Tested on a water tank system, the approach proves effective in behaviour prediction and proactive fault handling, enhancing CPS design for robust, secure, and fault-tolerant systems.

关键词： Cyber-Physical System Fault Modelling Dynamic Behavioural Prediction Fault Injection

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A Comparative analysis of Tools & Task Types for Measuring Computational Problem-Solving 2024

A Comparative Analysis of Tools & Task Types for Measuring C...

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

作者： Bumbacher, Engin Brender, Jérôme Davis, Richard Lee Vaud Lausanne Switzerland

ISBN: (纸本)9798400704246

How to measure students' Computational Problem-Solving (CPS) competencies is an ongoing research topic. Prevalent approaches vary by measurement tools (e.g., interactive programming, multiple-choice tests, or programming-independent tests) and task types (e.g., debugging problems or Parson problems). However, few studies have examined the measurement tools of CPS competencies themselves: affordances and limitations of the measurement tools and how they compare, or whether different task types might elicit CPS competencies differently. Research needs to address these questions in order to better understand how to design robust, generalizable, and effective measurement tools for CPS competencies. This paper presents an exploratory study that contributes to this research direction. It is part of a larger international project to develop an open-access formative assessment platform for CPS, which includes a novel authoring tool for a wide range of task types for interactive block-based programming. We used the tool to create an interactive programming experience with multiple task types and gave it to more than 300 secondary school students from different countries. We also administered a validated multiple-choice measurement of Computational Thinking with block-based programs. We focused on task complexity as a characteristic of task type, using a classification scheme based on task design features. Comparing students' performances on tasks of different complexity and using two distinct measurement tools, we found that the multiple-choice measurement only partially predicts performance in the interactive programming task. Additionally, its predictive capacity varies significantly between task types of differing complexity. © 2024 Owner/Author.

关键词： Students

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