Margaret Martonosi

Bio: Margaret Martonosi is an academic researcher from Princeton University. The author has contributed to research in topics: Cache & Quantum computer. The author has an hindex of 71, co-authored 277 publications receiving 23162 citations. Previous affiliations of Margaret Martonosi include Harvard University & National Science Foundation.

Wattch: a framework for architectural-level power analysis and optimizations

Abstract: Power dissipation and thermal issues are increasingly significant in modern processors. As a result, it is crucial that power/performance tradeoffs be made more visible to chip architects and even compiler writers, in addition to circuit designers. Most existing power analysis tools achieve high accuracy by calculating power estimates for designs only after layout or floorplanning are complete. In addition to being available only late in the design process, such tools are often quite slow, which compounds the difficulty of running them for a large space of design possibilities.This paper presents Wattch, a framework for analyzing and optimizing microprocessor power dissipation at the architecture-level. Wattch is 1000X or more faster than existing layout-level power tools, and yet maintains accuracy within 10% of their estimates as verified using industry tools on leading-edge designs. This paper presents several validations of Wattch's accuracy. In addition, we present three examples that demonstrate how architects or compiler writers might use Wattch to evaluate power consumption in their design process.We see Wattch as a complement to existing lower-level tools; it allows architects to explore and cull the design space early on, using faster, higher-level tools. It also opens up the field of power-efficient computing to a wider range of researchers by providing a power evaluation methodology within the portable and familiar SimpleScalar framework.

Energy-efficient computing for wildlife tracking: design tradeoffs and early experiences with ZebraNet

Abstract: Over the past decade, mobile computing and wireless communication have become increasingly important drivers of many new computing applications. The field of wireless sensor networks particularly focuses on applications involving autonomous use of compute, sensing, and wireless communication devices for both scientific and commercial purposes. This paper examines the research decisions and design tradeoffs that arise when applying wireless peer-to-peer networking techniques in a mobile sensor network designed to support wildlife tracking for biology research.The ZebraNet system includes custom tracking collars (nodes) carried by animals under study across a large, wild area; the collars operate as a peer-to-peer network to deliver logged data back to researchers. The collars include global positioning system (GPS), Flash memory, wireless transceivers, and a small CPU; essentially each node is a small, wireless computing device. Since there is no cellular service or broadcast communication covering the region where animals are studied, ad hoc, peer-to-peer routing is needed. Although numerous ad hoc protocols exist, additional challenges arise because the researchers themselves are mobile and thus there is no fixed base station towards which to aim data. Overall, our goal is to use the least energy, storage, and other resources necessary to maintain a reliable system with a very high `data homing' success rate. We plan to deploy a 30-node ZebraNet system at the Mpala Research Centre in central Kenya. More broadly, we believe that the domain-centric protocols and energy tradeoffs presented here for ZebraNet will have general applicability in other wireless and sensor applications.

Dynamic thermal management for high-performance microprocessors

Abstract: With the increasing clock rate and transistor count of today's microprocessors, power dissipation is becoming a critical component of system design complexity. Thermal and power-delivery issues are becoming especially critical for high-performance computing systems. In this work, we investigate dynamic thermal management as a technique to control CPU power dissipation. With the increasing usage of clock gating techniques, the average power dissipation typically seen by common applications is becoming much less than the chip's rated maximum power dissipation. However system designers still must design thermal heat sinks to withstand the worse-case scenario. We define and investigate the major components of any dynamic thermal management scheme. Specifically we explore the tradeoffs between several mechanisms for responding to periods of thermal trauma and we consider the effects of hardware and software implementations. With approximate dynamic thermal management, the CPU can be designed for a much lower maximum power rating, with minimal performance impact for typical applications.

Cache decay: exploiting generational behavior to reduce cache leakage power

Abstract: Power dissipation is increasingly important in CPUs ranging from those intended for mobile use, all the way up to high-performance processors for high-end servers. While the bulk of the power dissipated is dynamic switching power, leakage power is also beginning to be a concern. Chipmakers expect that in future chip generations, leakage's proportion of total chip power will increase significantly.This paper examines methods for reducing leakage power within the cache memories of the CPU. Because caches comprise much of a CPU chip's area and transistor counts, they are reasonable targets for attacking leakage. We discuss policies and implementations for reducing cache leakage by invalidating and “turning off” cache lines when they hold data not likely to be reused. In particular, our approach is targeted at the generational nature of cache line usage. That is, cache lines typically have a flurry of frequent use when first brought into the cache, and then have a period of “dead time” before they are evicted. By devising effective, low-power ways of deducing dead time, our results show that in many cases we can reduce LI cache leakage energy by 4x in SPEC2000 applications without impacting performance. Because our decay-based techniques have notions of competitive on-line algorithms at their roots, their energy usage can be theoretically bounded at within a factor of two of the optimal oracle-based policy. We also examine adaptive decay-based policies that make energy-minimizing policy choices on a per-application basis by choosing appropriate decay intervals individually for each cache line. Our proposed adaptive policies effectively reduce LI cache leakage energy by 5x for the SPEC2000 with only negligible degradations in performance.

An Analysis of Efficient Multi-Core Global Power Management Policies: Maximizing Performance for a Given Power Budget

Abstract: Chip-level power and thermal implications will continue to rule as one of the primary design constraints and performance limiters. The gap between average and peak power actually widens with increased levels of core integration. As such, if per-core control of power levels (modes) is possible, a global power manager should be able to dynamically set the modes suitably. This would be done in tune with the workload characteristics, in order to always maintain a chip-level power that is below the specified budget. Furthermore, this should be possible without significant degradation of chip-level throughput performance. We analyze and validate this concept in detail in this paper. We assume a per-core DVFS (dynamic voltage and frequency scaling) knob to be available to such a conceptual global power manager. We evaluate several different policies for global multi-core power management. In this analysis, we consider various different objectives such as prioritization and optimized throughput. Overall, our results show that in the context of a workload comprised of SPEC benchmark threads, our best architected policies can come within 1% of the performance of an ideal oracle, while meeting a given chip-level power budget. Furthermore, we show that these global dynamic management policies perform significantly better than static management, even if static scheduling is given oracular knowledge.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

A delay-tolerant network architecture for challenged internets

Abstract: The highly successful architecture and protocols of today's Internet may operate poorly in environments characterized by very long delay paths and frequent network partitions. These problems are exacerbated by end nodes with limited power or memory resources. Often deployed in mobile and extreme environments lacking continuous connectivity, many such networks have their own specialized protocols, and do not utilize IP. To achieve interoperability between them, we propose a network architecture and application interface structured around optionally-reliable asynchronous message forwarding, with limited expectations of end-to-end connectivity and node resources. The architecture operates as an overlay above the transport layers of the networks it interconnects, and provides key services such as in-network data storage and retransmission, interoperable naming, authenticated forwarding and a coarse-grained class of service.

Principles and Practices of Interconnection Networks

Abstract: One of the greatest challenges faced by designers of digital systems is optimizing the communication and interconnection between system components. Interconnection networks offer an attractive and economical solution to this communication crisis and are fast becoming pervasive in digital systems. Current trends suggest that this communication bottleneck will be even more problematic when designing future generations of machines. Consequently, the anatomy of an interconnection network router and science of interconnection network design will only grow in importance in the coming years. This book offers a detailed and comprehensive presentation of the basic principles of interconnection network design, clearly illustrating them with numerous examples, chapter exercises, and case studies. It incorporates hardware-level descriptions of concepts, allowing a designer to see all the steps of the process from abstract design to concrete implementation. ·Case studies throughout the book draw on extensive author experience in designing interconnection networks over a period of more than twenty years, providing real world examples of what works, and what doesn't. ·Tightly couples concepts with implementation costs to facilitate a deeper understanding of the tradeoffs in the design of a practical network. ·A set of examples and exercises in every chapter help the reader to fully understand all the implications of every design decision. Table of Contents Chapter 1 Introduction to Interconnection Networks 1.1 Three Questions About Interconnection Networks 1.2 Uses of Interconnection Networks 1.3 Network Basics 1.4 History 1.5 Organization of this Book Chapter 2 A Simple Interconnection Network 2.1 Network Specifications and Constraints 2.2 Topology 2.3 Routing 2.4 Flow Control 2.5 Router Design 2.6 Performance Analysis 2.7 Exercises Chapter 3 Topology Basics 3.1 Nomenclature 3.2 Traffic Patterns 3.3 Performance 3.4 Packaging Cost 3.5 Case Study: The SGI Origin 2000 3.6 Bibliographic Notes 3.7 Exercises Chapter 4 Butterfly Networks 4.1 The Structure of Butterfly Networks 4.2 Isomorphic Butterflies 4.3 Performance and Packaging Cost 4.4 Path Diversity and Extra Stages 4.5 Case Study: The BBN Butterfly 4.6 Bibliographic Notes 4.7 Exercises Chapter 5 Torus Networks 5.1 The Structure of Torus Networks 5.2 Performance 5.3 Building Mesh and Torus Networks 5.4 Express Cubes 5.5 Case Study: The MIT J-Machine 5.6 Bibliographic Notes 5.7 Exercises Chapter 6 Non-Blocking Networks 6.1 Non-Blocking vs. Non-Interfering Networks 6.2 Crossbar Networks 6.3 Clos Networks 6.4 Benes Networks 6.5 Sorting Networks 6.6 Case Study: The Velio VC2002 (Zeus) Grooming Switch 6.7 Bibliographic Notes 6.8 Exercises Chapter 7 Slicing and Dicing 7.1 Concentrators and Distributors 7.2 Slicing and Dicing 7.3 Slicing Multistage Networks 7.4 Case Study: Bit Slicing in the Tiny Tera 7.5 Bibliographic Notes 7.6 Exercises Chapter 8 Routing Basics 8.1 A Routing Example 8.2 Taxonomy of Routing Algorithms 8.3 The Routing Relation 8.4 Deterministic Routing 8.5 Case Study: Dimension-Order Routing in the Cray T3D 8.6 Bibliographic Notes 8.7 Exercises Chapter 9 Oblivious Routing 9.1 Valiant's Randomized Routing Algorithm 9.2 Minimal Oblivious Routing 9.3 Load-Balanced Oblivious Routing 9.4 Analysis of Oblivious Routing 9.5 Case Study: Oblivious Routing in the Avici Terabit Switch Router(TSR) 9.6 Bibliographic Notes 9.7 Exercises Chapter 10 Adaptive Routing 10.1 Adaptive Routing Basics 10.2 Minimal Adaptive Routing 10.3 Fully Adaptive Routing 10.4 Load-Balanced Adaptive Routing 10.5 Search-Based Routing 10.6 Case Study: Adaptive Routing in the Thinking Machines CM-5 10.7 Bibliographic Notes 10.8 Exercises Chapter 11 Routing Mechanics 11.1 Table-Based Routing 11.2 Algorithmic Routing 11.3 Case Study: Oblivious Source Routing in the IBM Vulcan Network 11.4 Bibliographic Notes 11.5 Exercises Chapter 12 Flow Control Basics 12.1 Resources and Allocation Units 12.2 Bufferless Flow Control 12.3 Circuit Switching 12.4 Bibliographic Notes 12.5 Exercises Chapter 13 Buffered Flow Control 13.1 Packet-Buffer Flow Control 13.2 Flit-Buffer Flow Control 13.3 Buffer Management and Backpressure 13.4 Flit-Reservation Flow Control 13.5 Bibliographic Notes 13.6 Exercises Chapter 14 Deadlock and Livelock 14.1 Deadlock 14.2 Deadlock Avoidance 14.3 Adaptive Routing 14.4 Deadlock Recovery 14.5 Livelock 14.6 Case Study: Deadlock Avoidance in the Cray T3E 14.7 Bibliographic Notes 14.8 Exercises Chapter 15 Quality of Service 15.1 Service Classes and Service Contracts 15.2 Burstiness and Network Delays 15.3 Implementation of Guaranteed Services 15.4 Implementation of Best-Effort Services 15.5 Separation of Resources 15.6 Case Study: ATM Service Classes 15.7 Case Study: Virtual Networks in the Avici TSR 15.8 Bibliographic Notes 15.9 Exercises Chapter 16 Router Architecture 16.1 Basic Router Architecture 16.2 Stalls 16.3 Closing the Loop with Credits 16.4 Reallocating a Channel 16.5 Speculation and Lookahead 16.6 Flit and Credit Encoding 16.7 Case Study: The Alpha 21364 Router 16.8 Bibliographic Notes 16.9 Exercises Chapter 17 Router Datapath Components 17.1 Input Buffer Organization 17.2 Switches 17.3 Output Organization 17.4 Case Study: The Datapath of the IBM Colony Router 17.5 Bibliographic Notes 17.6 Exercises Chapter 18 Arbitration 18.1 Arbitration Timing 18.2 Fairness 18.3 Fixed Priority Arbiter 18.4 Variable Priority Iterative Arbiters 18.5 Matrix Arbiter 18.6 Queuing Arbiter 18.7 Exercises Chapter 19 Allocation 19.1 Representations 19.2 Exact Algorithms 19.3 Separable Allocators 19.4 Wavefront Allocator 19.5 Incremental vs. Batch Allocation 19.6 Multistage Allocation 19.7 Performance of Allocators 19.8 Case Study: The Tiny Tera Allocator 19.9 Bibliographic Notes 19.10 Exercises Chapter 20 Network Interfaces 20.1 Processor-Network Interface 20.2 Shared-Memory Interface 20.3 Line-Fabric Interface 20.4 Case Study: The MIT M-Machine Network Interface 20.5 Bibliographic Notes 20.6 Exercises Chapter 21 Error Control 411 21.1 Know Thy Enemy: Failure Modes and Fault Models 21.2 The Error Control Process: Detection, Containment, and Recovery 21.3 Link Level Error Control 21.4 Router Error Control 21.5 Network-Level Error Control 21.6 End-to-end Error Control 21.7 Bibliographic Notes 21.8 Exercises Chapter 22 Buses 22.1 Bus Basics 22.2 Bus Arbitration 22.3 High Performance Bus Protocol 22.4 From Buses to Networks 22.5 Case Study: The PCI Bus 22.6 Bibliographic Notes 22.7 Exercises Chapter 23 Performance Analysis 23.1 Measures of Interconnection Network Performance 23.2 Analysis 23.3 Validation 23.4 Case Study: Efficiency and Loss in the BBN Monarch Network 23.5 Bibliographic Notes 23.6 Exercises Chapter 24 Simulation 24.1 Levels of Detail 24.2 Network Workloads 24.3 Simulation Measurements 24.4 Simulator Design 24.5 Bibliographic Notes 24.6 Exercises Chapter 25 Simulation Examples 495 25.1 Routing 25.2 Flow Control Performance 25.3 Fault Tolerance Appendix A Nomenclature Appendix B Glossary Appendix C Network Simulator

Locating the nodes: cooperative localization in wireless sensor networks

Abstract: Accurate and low-cost sensor localization is a critical requirement for the deployment of wireless sensor networks in a wide variety of applications. In cooperative localization, sensors work together in a peer-to-peer manner to make measurements and then forms a map of the network. Various application requirements influence the design of sensor localization systems. In this article, the authors describe the measurement-based statistical models useful to describe time-of-arrival (TOA), angle-of-arrival (AOA), and received-signal-strength (RSS) measurements in wireless sensor networks. Wideband and ultra-wideband (UWB) measurements, and RF and acoustic media are also discussed. Using the models, the authors have shown the calculation of a Cramer-Rao bound (CRB) on the location estimation precision possible for a given set of measurements. The article briefly surveys a large and growing body of sensor localization algorithms. This article is intended to emphasize the basic statistical signal processing background necessary to understand the state-of-the-art and to make progress in the new and largely open areas of sensor network localization research.