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arXiv 2021年

作者： Chadha, Mohak Gerndt, Michael Computer Architecture and Parallel Systems Technische Universität München Garching [near Munich Germany

—Energy efficiency and energy conservation are one of the most crucial constraints for meeting the 20MW power envelope desired for exascale systems. Towards this, most of the research in this area has been focused on the utilization of user-controllable hardware switches such as per-core dynamic voltage frequency scaling (DVFS) and software controlled clock modulation at the application level. In this paper, we present a tuning plugin for the Periscope Tuning Framework which integrates fine-grained autotuning at the region level with DVFS and uncore frequency scaling (UFS). The tuning is based on a feed-forward neural network which is formulated using Performance Monitoring Counters (PMC) supported by x86 systems and trained using standardized benchmarks. Experiments on five standardized hybrid benchmarks show an energy improvement of 16.1% on average when the applications are tuned according to our methodology as compared to 7.8% for static tuning. Copyright © 2021, The Authors. All rights reserved.

关键词： Dynamic frequency scaling

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architecture-specific performance optimization of compute-intensive FaaS functions

arXiv

引用

arXiv 2021年

作者： Chadha, Mohak Jindal, Anshul Gerndt, Michael Computer Architecture and Parallel Systems Technische Universität München Garching [near Munich Germany

FaaS allows an application to be decomposed into functions that are executed on a FaaS platform. The FaaS platform is responsible for the resource provisioning of the functions. Recently, there is a growing trend towards the execution of compute-intensive FaaS functions that run for several seconds. However, due to the billing policies followed by commercial FaaS offerings, the execution of these functions can incur significantly higher costs. Moreover, due to the abstraction of underlying processor architectures on which the functions are executed, the optimization of these functions is challenging. As a result, most FaaS functions use pre-compiled libraries generic to x86-64 leading to performance degradation. In this paper, we examine the underlying processor architectures for Google Cloud Functions (GCF) and determine their prevalence across the 19 available GCF regions. We modify, adapt, and optimize a representative set of six compute-intensive FaaS workloads written in Python using Numba, a JIT compiler based on LLVM, and present results wrt performance, memory consumption, and costs on GCF. Results from our experiments show that the optimization of FaaS functions can improve performance by 18.2x (geometric mean) and save costs by 76.8% on average for the six functions. Our results show that optimization of the FaaS functions for the specific architecture is very important. We achieved a maximum speedup of 1.79x by tuning the function especially for the instruction set of the underlying processor architecture. Copyright © 2021, The Authors. All rights reserved.

关键词： Cost functions

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gFaaS: Enabling Generic Functions in Serverless Computing

gFaaS: Enabling Generic Functions in Serverless Computing

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IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Mohak Chadha Paul Wieland Michael Gerndt Chair of Computer Architecture and Parallel Systems Technische Universität München Garching (near Munich) Germany

ISBN: (数字)9798350330663

ISBN: (纸本)9798350330670

With the advent of AWS Lambda in 2014, Serverless Computing, particularly Function-as-a-Service (FaaS), has witnessed growing popularity across various application domains. FaaS enables an application to be decomposed into fine-grained functions that are executed on a FaaS platform. It offers several advantages such as no infrastructure management, a pay-per-use billing policy, and on-demand fine-grained autoscaling. However, despite its advantages, developers today encounter various challenges while adopting FaaS solutions that reduce productivity. These include FaaS platform lock-in, support for diverse function deployment parameters, and diverse interfaces for interacting with FaaS platforms. To address these challenges, we present gFaaS, a novel framework that facilitates the holistic development and management of functions across diverse FaaS platforms. Our framework enables the development of generic functions in multiple programming languages that can be seamlessly deployed across different platforms without modifications. Results from our experiments demonstrate that gFaaS functions perform similarly to native platform-specific functions across various scenarios. A video demonstrating the functioning of gFaaS is available from https://***/STbb6ykJFf0.

关键词： Productivity computer languages Serverless computing Software

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FedLess: Secure and Scalable Federated Learning Using Serverless Computing

FedLess: Secure and Scalable Federated Learning Using Server...

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IEEE International Conference on Big Data

作者： Andreas Grafberger Mohak Chadha Anshul Jindal Jianfeng Gu Michael Gerndt Chair of Computer Architecture and Parallel Systems Technische Universität München Garching (Near Munich) Germany

ISBN: (纸本)9781665445993

The traditional cloud-centric approach for Deep Learning (DL) requires training data to be collected and processed at a central server which is often challenging in privacy-sensitive domains like healthcare. Towards this, a new learning paradigm called Federated Learning (FL) has been proposed that brings the potential of DL to these domains while addressing privacy and data ownership issues. FL enables clients to learn a shared ML model while keeping the data local. However, conventional FL systems face challenges such as scalability, complex infrastructure management, and wasted compute and incurred costs due to idle clients. These challenges of FL systems closely align with the core problems that serverless computing and Function-as-a-Service (FaaS) platforms aim to solve. These include rapid scalability, no infrastructure management, automatic scaling to zero for idle clients, and a pay-per-use billing model. To this end, we present a novel system and framework for serverless FL, called FedLess. Our system supports multiple commercial and self-hosted FaaS providers and can be deployed in the cloud, on-premise in institutional data centers, and on edge devices. To the best of our knowledge, we are the first to enable FL across a large fabric of heterogeneous FaaS providers while providing important features like security and Differential Privacy. We demonstrate with comprehensive experiments that the successful training of DNNs for different tasks across up to 200 client functions and more is easily possible using our system. Furthermore, we demonstrate the practical viability of our methodology by comparing it against a traditional FL system and show that it can be cheaper and more resource-efficient.

关键词： Training Scalability Computational modeling Serverless computing Training data FAA Big Data

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Extending SLURM for Dynamic Resource-Aware Adaptive Batch Scheduling

Extending SLURM for Dynamic Resource-Aware Adaptive Batch Sc...

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International Conference on High Performance Computing

作者： Mohak Chadha Jophin John Michael Gerndt Chair of Computer Architecture and Parallel Systems Technische Universität München Garching (near Munich) Germany

ISBN: (纸本)9781665422925;9781665446501

With the growing constraints on power budget and increasing hardware failure rates, the operation of future exascale systems faces several challenges. Towards this, resource awareness and adaptivity by enabling malleable jobs has been actively researched in the HPC community. Malleable jobs can change their computing resources at runtime and can significantly improve HPC system performance. However, due to the rigid nature of popular parallel programming paradigms such as MPI and lack of support for dynamic resource management in batch systems, malleable jobs have been largely unrealized. In this paper, we extend the SLURM batch system to support the execution and batch scheduling of malleable jobs. The malleable applications are written using a new adaptive parallel paradigm called Invasive MPI which extends the MPI standard to support resource-adaptivity at runtime. We propose two malleable job scheduling strategies to support performance-aware and power-aware dynamic reconfiguration decisions at runtime. We implement the strategies in SLURM and evaluate them on a production HPC system. Results for our performance-aware scheduling strategy show improvements in makespan, average system utilization, average response, and waiting times as compared to other scheduling strategies. Moreover, we demonstrate dynamic power corridor management using our power-aware strategy.

关键词： Runtime Processor scheduling parallel programming System performance High performance computing Production Dynamic scheduling

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PreCrime to the rescue: Defeating mobile malware one-step ahead 14

PreCrime to the rescue: Defeating mobile malware one-step ah...

引用

5th ACM Asia-Pacific Workshop on systems, APSYS 2014

作者： Tan, Cheng Li, Haibo Xia, Yubin Zang, Binyu Chu, Cheng-Kang Li, Tieyan Institute of Parallel and Distributed Systems Shanghai Jiao Tong University China Software School Fudan University China State Key Laboratory of Computer Architecture ICT Chinese Academy of Sciences China Huawei Technologies Pte Ltd. Singapore Singapore

ISBN: (纸本)9781450330244

Prior mobile malware defensive means is usually retroactive, which may either lead to high false negatives or can hardly recover systems states from malware activities. PreCrime is a proactive malware detection scheme that detects and stops malware activities from happening. PreCrime creates mirrors of a mobile device in a resource-rich and trusted cloud, which speculatively executes multiple likely user operations concurrently to detect potential tampering and information leakage. Our preliminary evaluation shows that PreCrime introduces small performance overhead on smartphones and feasible delay during speculative execution on the cloud. © 2014 ACM.

关键词： Malware

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Scalable Infrastructure for Workload Characterization of Cluster Traces

arXiv

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arXiv 2022年

作者： van Loo, Thomas Jindal, Anshul Benedict, Shajulin Chadha, Mohak Gerndt, Michael Computer Architecture and Parallel Systems Technical University Munich Germany Indian Institute of Information Technology Kerala Kottayam India

In the recent past, characterizing workloads has been attempted to gain a foothold in the emerging serverless cloud market, especially in the large production cloud clusters of Google, AWS, and so forth. While analyzing and characterizing real workloads from a large production cloud cluster benefits cloud providers, researchers, and daily users, analyzing the workload traces of these clusters has been an arduous task due to the heterogeneous nature of data. This article proposes a scalable infrastructure based on Google’s dataproc for analyzing the workload traces of cloud environments. We evaluated the functioning of the proposed infrastructure using the workload traces of Google cloud cluster-usage-traces-v3. We perform the workload characterization on this dataset, focusing on the heterogeneity of the workload, the variations in job durations, aspects of resources consumption, and the overall availability of resources provided by the cluster. The findings reported in the paper will be beneficial for cloud infrastructure providers and users while managing the cloud computing resources, especially serverless platforms. © 2022, CC BY.

关键词： Cluster computing

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Cuckoo directory: A scalable directory for many-core systems

Cuckoo directory: A scalable directory for many-core systems

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IEEE Symposium on High-Performance computer architecture

作者： Michael Ferdman Pejman Lotfi-Kamran Ken Balet Babak Falsafi Computer Architecture Lab Carnegie Mellon University USA Parallel Systems Architecture Lab école Polytechnique Fédérale de Lausanne Switzerland

Growing core counts have highlighted the need for scalable on-chip coherence mechanisms. The increase in the number of on-chip cores exposes the energy and area costs of scaling the directories. Duplicate-tag-based directories require highly associative structures that grow with core count, precluding scalability due to prohibitive power consumption. Sparse directories overcome the power barrier by reducing directory associativity, but require storage area over-provisioning to avoid high invalidation rates. We propose the Cuckoo directory, a power- and area-efficient scalable distributed directory. The cuckoo directory scales to high core counts without the energy costs of wide associative lookup and without gross capacity over-provisioning. Simulation of a 16-core CMP with commercial server and scientific workloads shows that the Cuckoo directory eliminates invalidations while being up to four times more power-efficient than the Duplicate-tag directory and 24% more power-efficient and up to seven times more area-efficient than the Sparse directory organization. Analytical projections indicate that the Cuckoo directory retains its energy and area benefits with increasing core count, efficiently scaling to at least 1024 cores.

关键词： Organizations Coherence Scalability Aggregates System-on-a-chip Servers Hardware

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Asynchronous Runtimes in Action: An Introspective Framework for a Next Gen Runtime

Asynchronous Runtimes in Action: An Introspective Framework ...

引用

IEEE International Symposium on parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Joshua Suetterlein Joshua Landwehr Andrés Márquez Joseph B. Manzano Guang R. Gao The Computer Architecture and Parallel Systems Laboratory University of Delaware Newark Delaware Pacific Northwest National Laboratory Richland Washington

ISBN: (纸本)9781509036837

One of the most critical challenges that new highperformance systems face is the lack of system software supportfor these large scale systems. Investment on system stack componentsis essential in the development, debugging and optimizationof the new emerging programming models. These emergingmodels have the promise to better utilize the vast hardwareresources available in current and future systems. To aid in thedevelopment of applications and new system stacks, runtimes, asinstances of their respective execution models, need to producefacilities to introspect their inner workings and allow an indepthattribution of performance bottlenecks and computationalpatterns. In other words, the runtime systems need to reducetheir opacity to observers so that users of a novel programexecution model can adapt their designs to fit the intended modelusage, regardless of the layer that they are working on. Thisdesign/development loop (akin to co-design) enables synergisticopportunities across the entire computational stack. This paper presents the design and implementation of a simple"gray" box performance attribution harness running inside a finegrain runtime system: the Open Community Runtime (OCR). We showcase what such a framework can indicate regarding theruntime behavior while running at scale. To this end, we havedesigned a set of synthetic scenarios aimed to test the runtime attheir best and worst cases. We present an analysis of the mostimportant runtime features, properties and idiosyncrasies thatwill affect the development of new runtime features, algorithmicselection, and application development.

关键词： Runtime Optical character recognition software Instruction sets Computational modeling Adaptation models Message systems Protocols

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Towards Autoscaling with Guarantees on Kubernetes Clusters

Towards Autoscaling with Guarantees on Kubernetes Clusters

引用

Autonomic Computing and Self-Organizing systems Companion (ACSOS-C), IEEE International Conference on

作者： Stephen Burroughs Helge Dickel Martin van Zijl Vladimir Podolskiy Michael Gerndt Robi Malik Panos Patros ORKA Cloud and Adaptive Systems Lab University of Waikato New Zealand Chair of Computer Architecture and Parallel Systems Technical University of Munich Germany

Autoscaling is used by cloud providers, microser-vices, and edge computing applications to respond to dynamic load fluctuations. A critical direction of research has focused on providing guarantees under uncertainty that the auto scaling system will work as intended-both at design-time and more importantly, at runtime. In this work, we evaluate the efficacy of three complementary methods: A) deterministic finite automata, B) probabilistic process algebra and C) Proportional-Integral-Derivative (PID) control. We experimentally evaluate their efficacy in modelling and verify autoscaling on clusters managed by Kubernetes. Our results suggest that deterministic modelling can provide theoretically optimal guarantees for small deployments; probabilistic algebras are able to capture stochastic behaviours, but benefit from deterministic templates; and control theory benefits by providing a black-box approach for modelling, verification and control.

关键词：

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