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EDAML 2022 Invited Speaker 3: Scalable ML Architectures for Real-time Energy-efficient computing

EDAML 2022 Invited Speaker 3: Scalable ML Architectures for ...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： R. Iris Bahar Colorado School of Mines

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Technological advancements have led to a proliferation machine learning systems to assist humans in a wide range of tasks. However, we are still far from accurate, reliable, and resource-efficient operations for many of these systems. Despite the strengths of convolutional neural networks (CNNs) for object recognition, these discriminative techniques have several shortcomings that leave them vulnerable to exploitation from adversaries. In addition, the computational cost incurred to train these discriminative models can be quite significant. Discriminative-generative approaches offers a promising avenue for robust perception and action. Such methods combine inference by deep learning with sampling and probabilistic inference models to achieve robust and adaptive understanding. In this talk, I will present our work on implementing a scalable, computationally efficient generative inference algorithm in hardware that can achieve real-time results in an energy efficient manner. I will also discuss future directions in designing scalable and efficient ML algorithms in hardware more broadly.

关键词： Adaptation models Computational modeling Probabilistic logic Inference algorithms Hardware Real-time systems Computational efficiency

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Unsupervised User Stance Detection on Tweets Against Web Articles Using Sentence Transformers

Unsupervised User Stance Detection on Tweets Against Web Art...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Bhashithe Abeysinghe Gyandeep Reddy Vulupala Anu G. Bourgeois Rajshekhar Sunderraman Department of Computer Science Georgia State University Atlanta GA USA

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Social media is an integral part of our daily lives, allowing users share opinions and content easily. This has been beneficial in many ways, but has also become a source for sharing misinformation and disinformation. Users can take different stances on various news or story articles that are being shared online. Predicting or detecting the stance of a user is a challenging problem in the Natural Language Processing and Inference circles, yet there is little work on the disinformation front. Infering users stance on disinformation is an important during a global pandemic. In this work we use a self-curated dataset to detect the stance of a Twitter user, based on a tweet and an article that they shared along with the tweet. Our process of collecting and curating the large dataset involves parallel computing methods to improve the timing performance. We present a novel unsupervised method for user stance detection that involves three phases: 1) embedding text from articles and tweets using a sentence transformer, 2) MeanShift clustering the article embeddings to determine themes, and 3) performing cosine similarity to find the semantic similarity between the tweet and article. Results of our methodology are promising and provide multiple directions for future work.

关键词： Couplings Social networking (online) Pandemics Semantics Media Linguistics parallel processing

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ETH: An Architecture for Exploring the Design Space of In-situ Scientific Visualization 34

ETH: An Architecture for Exploring the Design Space of In-si...

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34th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Abram, Gregory Adhinarayanan, Vignesh Feng, Wu-chun Rogers, David Ahrens, James Univ Texas Austin Texas Adv Comp Ctr Austin TX 78758 USA Virginia Tech Dept Comp Sci Blacksburg VA 24061 USA Los Alamos Natl Lab Comp Computat & Stat Sci Div Los Alamos NM 87545 USA

ISBN: (纸本)9781728168760

As high-performance computing (HPC) moves towards the exascale era, large-scale scientific simulations are generating enormous datasets. Many techniques (e.g., in-situ methods, data sampling, and compression) have been proposed to help visualize these large datasets under various constraints such as storage, power, and energy. However, evaluating these techniques and understanding the trade-offs (e.g., performance, efficiency, and quality) remains a challenging task. To enable exploration of the design space across such tradeoffs, we propose the Exploration Test Harness (ETH), an architecture for the early-stage exploration of visualization and rendering approaches, job layout, and visualization pipelines. ETH covers a broader parameter space than current large-scale visualization applications such as ParaView and VisIt. It also promotes the study of simulation-visualization coupling strategies through a data-centric approach, rather than requiring coupling with a specific scientific simulation code. Furthermore, with experimentation on an extensively instrumented supercomputer, we study more metrics of interest than was previously possible. Importantly, ETH will help to answer important what-if scenarios and trade-off questions in the early stages of pipeline development, helping scientists to make informed choices about how to best couple a simulation code with visualization at extreme scale.

关键词： In-situ Techniques High-Performance computing Design-space Exploration Raycasting Energy Efficiency

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Spatial-Domain Resource Scheduling for Wireless computing and Energy Provision in Energy Self-Sustainable F-RAN 18

Spatial-Domain Resource Scheduling for Wireless Computing an...

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18th IEEE Int Symp on parallel and distributed Proc with Applicat (ISPA) / 10th IEEE Int Conf on Big Data and Cloud Comp (BDCloud) / IEEE Int Symp on Social Comp and Networking (SocialCom) / IEEE Int Conf on Sustainable Comp and Commun (SustainCom)

作者： Hu, Jie Shui, Tingyu Zhang, Wei Wu, Fan Yang, Kun Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England

ISBN: (纸本)9781665414852

Recently, with the massive and urgent requirements of access of IoT devices and the notion of self-sustainable network, fog radio access networks(F-RANs) is combined with simultaneous wireless information and power transfer(SWIPT). Apart from their caching capabilities, enhanced remote-radioheads are also equipped with computational units for processing computational tasks offloaded from the computing users (CUs). With the aid of SWIPT, energy users(EUs) and computing users(CUs) are able to harvest energy or execute computing tasks respectively and simultaneously. However, these users (generally speaking, IoT devices) are mostly latency and energy intensive. Thus, in a F-RAN whose characteristics are the computing ability converted from the cloud to fog node, usually the enhanced remote-radio-heads (eRRHs), the offloading from CUs to eRRHs with more computing resources is taken into consideration. Moreover, the returning of tasks from eRRHs also offers opportunities for EUs to harvest energy. As a result, the schedule of the resources of a F-RAN for task offloading is of vital importance. For the optimal allocation of the resources, the offloading strategy, power and bandwidth allocation are jointly optimized for a maximum energy gain. Our simulation results demonstrate that the proposed joint optimization improves the performance of the offloading computing F-RAN system.

关键词： F-RAN mobile edge computing simultaneous wireless information and power transfer computing offloading resource scheduling

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RIFFLE: A distributed Network Operating System with Re-configurability for Multiple Domains

RIFFLE: A Distributed Network Operating System with Re-confi...

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IEEE international symposium on parallel and distributed Processing with Applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Boyang Zhou Chunming Wu Shuangxi Chen Zhen Li Hong Fan Zhejiang Lab Intelligent Networks Institute Hangzhou China School of Intelligent Science and Technology Hangzhou Institute for Advanced Study UCAS Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Jiaxing Key Laboratory of Industrial Internet Jiaxing China Research Center for Intelligent Technology Standardization Zhejiang Lab Hangzhou China Department of Electrical Engineering Shanghai University of Electric Power Shanghai China

The distributed network control in software-defined networking is greatly challenged by the design demands of evolving a running distributed control plane (DCP), and by the complexity of handling the control state dynamics. In this paper, we propose RIFFLE to address the problem using a distributed network operating system approach. RIFFLE enables the evolvability of control states of DCP deployed on a global scale. RIFFLE handles spatial and temporal dynamics that occur when updating and migrating the distributed control states, which overcomes the critical barrier to the evolvability. It fills the gap by enabling temporal reconfigurability in DCP. Moreover, RIFFLE reduces the complexities of building and maintaining network control services in DCP by enabling componentization and abstracting the underlying network dynamics. We evaluated the prototype RIFFLE through experiments running in PlanetLab. The results show that the prototype scales well for 135 nodes. We also validate that RIFFLE ensures the continuity and low content request delay for the supported the information-centric network supported during cache update periods owing to the enabled evolvability.

关键词： Runtime Roads Decentralized control Information-centric networking Prototypes Ubiquitous computing Dynamic scheduling

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FPGA Based Emulation Environment for Neuromorphic Architectures 34

FPGA Based Emulation Environment for Neuromorphic Architectu...

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34th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Valancius, Spencer Richter, Edward Purdy, Ruben Rockowitz, Kris Inouye, Michael Mack, Joshua Kumbhare, Nirmal Fair, Kaitlin Mixter, John Akoglu, Ali Univ Arizona Dept Elect & Comp Engn Tucson AZ 85719 USA Air Force Res Labs Eglin AFB FL 32542 USA Raytheon Missile Syst Tucson AZ 85747 USA

ISBN: (纸本)9781728174457

Neuromorphic architectures such as IBM's TrueNorth and Intel's Loihi have been introduced as platforms for energy efficient spiking neural network execution. However, there is no framework that allows for rapidly experimenting with neuromorphic architectures and studying the trade space on hardware performance and network accuracy. Fundamentally, this creates a barrier to entry for hardware designers looking to explore neuromorphic architectures. In this paper we present an open-source FPGA based emulation environment for neuromorphic computing research. We prototype IBM's TrueNorth architecture as a reference design and discuss FPGA specific design decisions made when implementing and integrating it's core components. We conduct resource utilization analysis and realize a streaming-enabled TrueNorth architecture on the Zynq UltraScale+ MPSoC. We then perform functional verification by implementing networks for MNIST dataset and vector matrix multiplication (VMM) in our emulation environment and present an accuracy-based comparison based on the same networks generated using IBM's Compass simulation environment. We demonstrate the utility of our emulation environment for hardware designers and application engineers by altering the neuron behavior for VMM mapping, which is, to the best of our knowledge, not feasible with any other tool including IBM's Compass environment. The proposed parameterized and configurable emulation platform serves as a basis for expanding its features to support emerging architectures, studying hypothetical neuromorphic architectures, or rapidly converging to hardware configuration through incremental changes based on bottlenecks as they become apparent during application mapping process.

关键词： Neuromorphic computing Emulation FPGA

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A Self-Optimized Generic Workload Prediction Framework for Cloud computing 34

A Self-Optimized Generic Workload Prediction Framework for C...

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34th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Jayakumar, Vinodh Kumaran Lee, Jaewoo Kim, In Kee Wang, Wei Univ Texas San Antonio Dept Comp Sci San Antonio TX 78249 USA Univ Georgia Dept Comp Sci Athens GA 30602 USA

ISBN: (数字)9781728168760

ISBN: (纸本)9781728168760

The accurate prediction of the future workload, such as the job arrival rate and the user request rate, is critical to the efficiency of resource management and elasticity in the cloud. However, designing a generic workload predictor that works properly for various types of workload is very challenging due to the large variety of workload patterns and the dynamic changes within a workload. Because of these challenges, existing workload predictors are usually hand-tuned for specific (types of) workloads for maximum accuracy. This necessity to individually tune the predictors also makes it very difficult to reproduce the results from prior research, as the predictor designs have a strong dependency on the workloads. In this paper, we present a novel generic workload prediction framework, LoadDynamics, that can provide high accuracy predictions for any workloads. LoadDynamics employs Long-Short-Term-Memory models and can automatically optimize its internal parameters for an individual workload to achieve high prediction accuracy. We evaluated LoadDynamics with a mixture of workload traces representing public cloud applications, scientific applications, data center jobs and web applications. The evaluation results show that LoadDynamics have only 18% prediction error on average, which is at least 6.7% lower than state-of-the-art workload prediction techniques. The error of LoadDynamics was also only 1% higher than the best predictor found by exhaustive search for each workload. When applied in the Google Cloud, LoadDynamics-enabled auto-scaling policy also outperformed the state-of-the-art predictors by reducing the job turnaround time by at least 24.6% and reducing virtual machine over-provisioning by at least 4.8%.

关键词： Cloud computing Workload Prediction Long Short-Term Memory Self-Optimized Framework Resource Management

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Ptlbmalloc2: Reducing TLB Shootdowns with High Memory Efficiency 18

Ptlbmalloc2: Reducing TLB Shootdowns with High Memory Effici...

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作者： Schildermans, Stijn Aerts, Kris Shan, Jianchen Ding, Xiaoning Katholieke Univ Leuven Dept Comp Sci Campus DiepenbeekWetenschapspk 27 B-3590 Diepenbeek Belgium Hofstra Univ Dept Comp Sci Hempstead NY 11549 USA New Jersey Inst Technol Dept Comp Sci Newark NJ 07102 USA

ISBN: (纸本)9781665414852

The cost of TLB consistency is steadily increasing as we evolve towards ever more parallel and consolidated systems. In many cases the application memory allocator is responsible for much of this cost. Existing allocators to our knowledge universally address this issue by sacrificing memory efficiency. This paper shows that such trade-offs are not necessary by presenting a novel memory allocator that exhibits both excellent memory efficiency and (TLB) scalability: ptlbmalloc2. First, we show that TLB consistency is becoming a major scalability bottleneck on modern systems. Next, we describe why existing memory allocators are unsatisfactory regarding this issue. Finally, we present and evaluate ptlbmalloc2, which has been implemeted as a library on top of glibc. Ptlbmalloc2 outperforms glibc by up to 70% in terms of cycles and execution time with a negligible impact on memory efficiency for real-world workloads. These results provide a strong incentive to rethink memory allocator scalability in the current era of many-core NUMA systems and cloud computing.

关键词： Cloud computing Scalability Memory management Libraries

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Learning Cost-Effective Sampling Strategies for Empirical Performance Modeling 34

Learning Cost-Effective Sampling Strategies for Empirical Pe...

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34th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Ritter, Marcus Calotoiu, Alexandru Rinke, Sebastian Reimann, Thorsten Hoefler, Torsten Wolf, Felix Tech Univ Darmstadt Dept Comp Sci Darmstadt Germany Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland

ISBN: (纸本)9781728168760

Identifying scalability bottlenecks in parallel applications is a vital but also laborious and expensive task. Empirical performance models have proven to be helpful to find such limitations, though they require a set of experiments in order to gain valuable insights. Therefore, the experiment design determines the quality and cost of the models. Extra-P is an empirical modeling tool that uses small-scale experiments to assess the scalability of applications. Its current version requires an exponential number of experiments per model parameter. This makes the creation of empirical performance models very expensive, and in some situations even impractical. In this paper, we propose a novel parameter-value selection heuristic, which functions as a guideline for the experiment design, leveraging sparse performance-modeling, a technique that only needs a polynomial number of experiments per model parameter. Using synthetic analysis and data from three different case studies, we show that our solution reduces the average modeling costs by about 85% while retaining 92% of the model accuracy.

关键词： Performance analysis performance modeling reinforcement learning high-performance computing parallel processing

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On the Complexity of Conditional DAG Scheduling in Multiprocessor Systems 34

On the Complexity of Conditional DAG Scheduling in Multiproc...

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34th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Marchetti-Spaccamela, Alberto Megow, Nicole Schloeter, Jens Skutella, Martin Stougie, Leen Sapienza Univ Rome Rome Italy INRIA Erable Paris France Univ Bremen Bremen Germany Tech Univ Berlin Berlin Germany Vrije Univ Amsterdam CWI Amsterdam Amsterdam Netherlands

ISBN: (纸本)9781728168760

As parallel processing became ubiquitous in modern computing systems, parallel task models have been proposed to describe the structure of parallel applications. The workflow scheduling problem has been studied extensively over past years, focusing on multiprocessor systems and distributed environments (e.g. grids, clusters). In workflow scheduling, applications are modeled as directed acyclic graphs (DAGs). DAGs have also been introduced in the real-time scheduling community to model the execution of multi-threaded programs on a multi-core architecture. The DAG model assumes, in most cases, a fixed DAG structure capturing only straight-line code. Only recently, more general models have been proposed. In particular, the conditional DAG model allows the presence of control structures such as conditional (if-then-else) constructs. While first algorithmic results have been presented for the conditional DAG model, the complexity of schedulability analysis remains wide open. We perform a thorough analysis on the worst-case makespan (latest completion time) of a conditional DAG task under list scheduling (a.k.a. fixed-priority scheduling). We show several hardness results concerning the complexity of the optimization problem on multiple processors, even if the conditional DAG has a well-nested structure. For general conditional DAG tasks, the problem is intractable even on a single processor. Complementing these negative results, we show that certain practice-relevant DAG structures are very well tractable.

关键词： parallel processing makespan conditional DAG complexity

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