Machine Learning on Accelerated Platforms Thomas Steinke
Total Page：16
File Type：pdf, Size：1020Kb
Load more
Recommended publications
-
Artificial Intelligence in Health Care: the Hope, the Hype, the Promise, the Peril
Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril Michael Matheny, Sonoo Thadaney Israni, Mahnoor Ahmed, and Danielle Whicher, Editors WASHINGTON, DC NAM.EDU PREPUBLICATION COPY - Uncorrected Proofs NATIONAL ACADEMY OF MEDICINE • 500 Fifth Street, NW • WASHINGTON, DC 20001 NOTICE: This publication has undergone peer review according to procedures established by the National Academy of Medicine (NAM). Publication by the NAM worthy of public attention, but does not constitute endorsement of conclusions and recommendationssignifies that it is the by productthe NAM. of The a carefully views presented considered in processthis publication and is a contributionare those of individual contributors and do not represent formal consensus positions of the authors’ organizations; the NAM; or the National Academies of Sciences, Engineering, and Medicine. Library of Congress Cataloging-in-Publication Data to Come Copyright 2019 by the National Academy of Sciences. All rights reserved. Printed in the United States of America. Suggested citation: Matheny, M., S. Thadaney Israni, M. Ahmed, and D. Whicher, Editors. 2019. Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. NAM Special Publication. Washington, DC: National Academy of Medicine. PREPUBLICATION COPY - Uncorrected Proofs “Knowing is not enough; we must apply. Willing is not enough; we must do.” --GOETHE PREPUBLICATION COPY - Uncorrected Proofs ABOUT THE NATIONAL ACADEMY OF MEDICINE The National Academy of Medicine is one of three Academies constituting the Nation- al Academies of Sciences, Engineering, and Medicine (the National Academies). The Na- tional Academies provide independent, objective analysis and advice to the nation and conduct other activities to solve complex problems and inform public policy decisions. -
AI Computer Wraps up 4-1 Victory Against Human Champion Nature Reports from Alphago's Victory in Seoul
The Go Files: AI computer wraps up 4-1 victory against human champion Nature reports from AlphaGo's victory in Seoul. Tanguy Chouard 15 March 2016 SEOUL, SOUTH KOREA Google DeepMind Lee Sedol, who has lost 4-1 to AlphaGo. Tanguy Chouard, an editor with Nature, saw Google-DeepMind’s AI system AlphaGo defeat a human professional for the first time last year at the ancient board game Go. This week, he is watching top professional Lee Sedol take on AlphaGo, in Seoul, for a $1 million prize. It’s all over at the Four Seasons Hotel in Seoul, where this morning AlphaGo wrapped up a 4-1 victory over Lee Sedol — incidentally, earning itself and its creators an honorary '9-dan professional' degree from the Korean Baduk Association. After winning the first three games, Google-DeepMind's computer looked impregnable. But the last two games may have revealed some weaknesses in its makeup. Game four totally changed the Go world’s view on AlphaGo’s dominance because it made it clear that the computer can 'bug' — or at least play very poor moves when on the losing side. It was obvious that Lee felt under much less pressure than in game three. And he adopted a different style, one based on taking large amounts of territory early on rather than immediately going for ‘street fighting’ such as making threats to capture stones. This style – called ‘amashi’ – seems to have paid off, because on move 78, Lee produced a play that somehow slipped under AlphaGo’s radar. David Silver, a scientist at DeepMind who's been leading the development of AlphaGo, said the program estimated its probability as 1 in 10,000. -
Raising the Bar for Using Gpus in Software Packet Processing Anuj Kalia and Dong Zhou, Carnegie Mellon University; Michael Kaminsky, Intel Labs; David G
Raising the Bar for Using GPUs in Software Packet Processing Anuj Kalia and Dong Zhou, Carnegie Mellon University; Michael Kaminsky, Intel Labs; David G. Andersen, Carnegie Mellon University https://www.usenix.org/conference/nsdi15/technical-sessions/presentation/kalia This paper is included in the Proceedings of the 12th USENIX Symposium on Networked Systems Design and Implementation (NSDI ’15). May 4–6, 2015 • Oakland, CA, USA ISBN 978-1-931971-218 Open Access to the Proceedings of the 12th USENIX Symposium on Networked Systems Design and Implementation (NSDI ’15) is sponsored by USENIX Raising the Bar for Using GPUs in Software Packet Processing Anuj Kalia, Dong Zhou, Michael Kaminsky∗, and David G. Andersen Carnegie Mellon University and ∗Intel Labs Abstract based implementations are far easier to experiment with) and in practice (software-based approaches are used for Numerous recent research efforts have explored the use low-speed applications and in cases such as forwarding of Graphics Processing Units (GPUs) as accelerators for within virtual switches [13]). software-based routing and packet handling applications, Our goal in this paper is to advance understanding of typically demonstrating throughput several times higher the advantages of GPU-assisted packet processors com- than using legacy code on the CPU alone. pared to CPU-only designs. In particular, noting that In this paper, we explore a new hypothesis about such several recent efforts have claimed that GPU-based de- designs: For many such applications, the benefits arise signs can be faster even for simple applications such as less from the GPU hardware itself as from the expression IPv4 forwarding [23, 43, 31, 50, 35, 30], we attempt to of the problem in a language such as CUDA or OpenCL identify the reasons for that speedup. -
In-Datacenter Performance Analysis of a Tensor Processing Unit
In-Datacenter Performance Analysis of a Tensor Processing Unit Presented by Josh Fried Background: Machine Learning Neural Networks: ● Multi Layer Perceptrons ● Recurrent Neural Networks (mostly LSTMs) ● Convolutional Neural Networks Synapse - each edge, has a weight Neuron - each node, sums weights and uses non-linear activation function over sum Propagating inputs through a layer of the NN is a matrix multiplication followed by an activation Background: Machine Learning Two phases: ● Training (offline) ○ relaxed deadlines ○ large batches to amortize costs of loading weights from DRAM ○ well suited to GPUs ○ Usually uses floating points ● Inference (online) ○ strict deadlines: 7-10ms at Google for some workloads ■ limited possibility for batching because of deadlines ○ Facebook uses CPUs for inference (last class) ○ Can use lower precision integers (faster/smaller/more efficient) ML Workloads @ Google 90% of ML workload time at Google spent on MLPs and LSTMs, despite broader focus on CNNs RankBrain (search) Inception (image classification), Google Translate AlphaGo (and others) Background: Hardware Trends End of Moore’s Law & Dennard Scaling ● Moore - transistor density is doubling every two years ● Dennard - power stays proportional to chip area as transistors shrink Machine Learning causing a huge growth in demand for compute ● 2006: Excess CPU capacity in datacenters is enough ● 2013: Projected 3 minutes per-day per-user of speech recognition ○ will require doubling datacenter compute capacity! Google’s Answer: Custom ASIC Goal: Build a chip that improves cost-performance for NN inference What are the main costs? Capital Costs Operational Costs (power bill!) TPU (V1) Design Goals Short design-deployment cycle: ~15 months! Plugs in to PCIe slot on existing servers Accelerates matrix multiplication operations Uses 8-bit integer operations instead of floating point How does the TPU work? CISC instructions, issued by host. -
Computer Architecture: Parallel Processing Basics
Computer Architecture: Parallel Processing Basics Onur Mutlu & Seth Copen Goldstein Carnegie Mellon University 9/9/13 Today What is Parallel Processing? Why? Kinds of Parallel Processing Multiprocessing and Multithreading Measuring success Speedup Amdhal’s Law Bottlenecks to parallelism 2 Concurrent Systems Embedded-Physical Distributed Sensor Claytronics Networks Concurrent Systems Embedded-Physical Distributed Sensor Claytronics Networks Geographically Distributed Power Internet Grid Concurrent Systems Embedded-Physical Distributed Sensor Claytronics Networks Geographically Distributed Power Internet Grid Cloud Computing EC2 Tashi PDL'09 © 2007-9 Goldstein5 Concurrent Systems Embedded-Physical Distributed Sensor Claytronics Networks Geographically Distributed Power Internet Grid Cloud Computing EC2 Tashi Parallel PDL'09 © 2007-9 Goldstein6 Concurrent Systems Physical Geographical Cloud Parallel Geophysical +++ ++ --- --- location Relative +++ +++ + - location Faults ++++ +++ ++++ -- Number of +++ +++ + - Processors + Network varies varies fixed fixed structure Network --- --- + + connectivity 7 Concurrent System Challenge: Programming The old joke: How long does it take to write a parallel program? One Graduate Student Year 8 Parallel Programming Again?? Increased demand (multicore) Increased scale (cloud) Improved compute/communicate Change in Application focus Irregular Recursive data structures PDL'09 © 2007-9 Goldstein9 Why Parallel Computers? Parallelism: Doing multiple things at a time Things: instructions, -
DISTRIBUTED COMPUTING ENVIRONMENT ABSTRACT The
DISTRIBUTED COMPUTING ENVIRONMENT ABSTRACT The high volume of networked computers, workstations, LANs has prompted users to move from a simple end user computing to a complex distributed computing environment. This transition is not just networking the computers, but also involves the issues of scalability, security etc. A Distributed Computing Environment herein referred to, as DCE is essentially an integration of all the services necessary to develop, support and manage a distributed computing environment. This term paper discusses the three important issues addressed by DCE in detail, Remote Procedure Calls [IRPC], Distributed File Systems [IDFS][OSF91] and Security [Isec][OSF92]. 1 INTRODUCTION The present day computing industry depends on the efficient usage of resources. So instead of duplicating the resources at every node of computing, a remote method of accessing the resources is more efficient and saves costs. This gave rise to the field of distributed computing, where not only physical resources, but also processing power was distributed. Distributed computing was driven by the following factors. a) Desire to share data and resources b) Minimize duplication of functionality c) Increase cost efficiency d) Increase reliability and availability of resources. The Open Source Foundation’s DCE (OSF DCE) has become the De facto standard for DCE applications and has the backing of IBM, COMPAQ, HP and the likes [OSFInter92]. 1.1 Why DCE? When an organization migrates from networked computing to distributed computing a lot of factors are to be taken into consideration. For example replication of files gives rise to consistency problems, clock synchronization becomes important, and security is a bigger consideration. -
Understanding & Generalizing Alphago Zero
Under review as a conference paper at ICLR 2019 UNDERSTANDING &GENERALIZING ALPHAGO ZERO Anonymous authors Paper under double-blind review ABSTRACT AlphaGo Zero (AGZ) (Silver et al., 2017b) introduced a new tabula rasa rein- forcement learning algorithm that has achieved superhuman performance in the games of Go, Chess, and Shogi with no prior knowledge other than the rules of the game. This success naturally begs the question whether it is possible to develop similar high-performance reinforcement learning algorithms for generic sequential decision-making problems (beyond two-player games), using only the constraints of the environment as the “rules.” To address this challenge, we start by taking steps towards developing a formal understanding of AGZ. AGZ includes two key innovations: (1) it learns a policy (represented as a neural network) using super- vised learning with cross-entropy loss from samples generated via Monte-Carlo Tree Search (MCTS); (2) it uses self-play to learn without training data. We argue that the self-play in AGZ corresponds to learning a Nash equilibrium for the two-player game; and the supervised learning with MCTS is attempting to learn the policy corresponding to the Nash equilibrium, by establishing a novel bound on the difference between the expected return achieved by two policies in terms of the expected KL divergence (cross-entropy) of their induced distributions. To extend AGZ to generic sequential decision-making problems, we introduce a robust MDP framework, in which the agent and nature effectively play a zero-sum game: the agent aims to take actions to maximize reward while nature seeks state transitions, subject to the constraints of that environment, that minimize the agent’s reward. -
Cost Optimization Pillar AWS Well-Architected Framework
Cost Optimization Pillar AWS Well-Architected Framework July 2020 This paper has been archived. The latest version is now available at: https://docs.aws.amazon.com/wellarchitected/latest/cost-optimization-pillar/welcome.htmlArchived Notices Customers are responsible for making their own independent assessment of the information in this document. This document: (a) is for informational purposes only, (b) represents current AWS product offerings and practices, which are subject to change without notice, and (c) does not create any commitments or assurances from AWS and its affiliates, suppliers or licensors. AWS products or services are provided “as is” without warranties, representations, or conditions of any kind, whether express or implied. The responsibilities and liabilities of AWS to its customers are controlled by AWS agreements, and this document is not part of, nor does it modify, any agreement between AWS and its customers. © 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved. Archived Contents Introduction .......................................................................................................................... 1 Cost Optimization ................................................................................................................ 2 Design Principles.............................................................................................................. 2 Definition .......................................................................................................................... -
AI for Broadcsaters, Future Has Already Begun…
AI for Broadcsaters, future has already begun… By Dr. Veysel Binbay, Specialist Engineer @ ABU Technology & Innovation Department 0 Dr. Veysel Binbay I have been working as Specialist Engineer at ABU Technology and Innovation Department for one year, before that I had worked at TRT (Turkish Radio and Television Corporation) for more than 20 years as a broadcast engineer, and also as an IT Director. I have wide experience on Radio and TV broadcasting technologies, including IT systems also. My experience includes to design, to setup, and to operate analogue/hybrid/digital radio and TV broadcast systems. I have also experienced on IT Networks. 1/25 What is Artificial Intelligence ? • Programs that behave externally like humans? • Programs that operate internally as humans do? • Computational systems that behave intelligently? 2 Some Definitions Trials for AI: The exciting new effort to make computers think … machines with minds, in the full literal sense. Haugeland, 1985 3 Some Definitions Trials for AI: The study of mental faculties through the use of computational models. Charniak and McDermott, 1985 A field of study that seeks to explain and emulate intelligent behavior in terms of computational processes. Schalkoff, 1990 4 Some Definitions Trials for AI: The study of how to make computers do things at which, at the moment, people are better. Rich & Knight, 1991 5 It’s obviously hard to define… (since we don’t have a commonly agreed definition of intelligence itself yet)… Lets try to focus to benefits, and solve definition problem later… 6 Brief history of AI 7 Brief history of AI . The history of AI begins with the following article: . -
Efficiently Mastering the Game of Nogo with Deep Reinforcement
electronics Article Efficiently Mastering the Game of NoGo with Deep Reinforcement Learning Supported by Domain Knowledge Yifan Gao 1,*,† and Lezhou Wu 2,† 1 College of Medicine and Biological Information Engineering, Northeastern University, Liaoning 110819, China 2 College of Information Science and Engineering, Northeastern University, Liaoning 110819, China; [email protected] * Correspondence: [email protected] † These authors contributed equally to this work. Abstract: Computer games have been regarded as an important field of artificial intelligence (AI) for a long time. The AlphaZero structure has been successful in the game of Go, beating the top professional human players and becoming the baseline method in computer games. However, the AlphaZero training process requires tremendous computing resources, imposing additional difficulties for the AlphaZero-based AI. In this paper, we propose NoGoZero+ to improve the AlphaZero process and apply it to a game similar to Go, NoGo. NoGoZero+ employs several innovative features to improve training speed and performance, and most improvement strategies can be transferred to other nonspecific areas. This paper compares it with the original AlphaZero process, and results show that NoGoZero+ increases the training speed to about six times that of the original AlphaZero process. Moreover, in the experiment, our agent beat the original AlphaZero agent with a score of 81:19 after only being trained by 20,000 self-play games’ data (small in quantity compared with Citation: Gao, Y.; Wu, L. Efficiently 120,000 self-play games’ data consumed by the original AlphaZero). The NoGo game program based Mastering the Game of NoGo with on NoGoZero+ was the runner-up in the 2020 China Computer Game Championship (CCGC) with Deep Reinforcement Learning limited resources, defeating many AlphaZero-based programs. -
AI Chips: What They Are and Why They Matter
APRIL 2020 AI Chips: What They Are and Why They Matter An AI Chips Reference AUTHORS Saif M. Khan Alexander Mann Table of Contents Introduction and Summary 3 The Laws of Chip Innovation 7 Transistor Shrinkage: Moore’s Law 7 Efficiency and Speed Improvements 8 Increasing Transistor Density Unlocks Improved Designs for Efficiency and Speed 9 Transistor Design is Reaching Fundamental Size Limits 10 The Slowing of Moore’s Law and the Decline of General-Purpose Chips 10 The Economies of Scale of General-Purpose Chips 10 Costs are Increasing Faster than the Semiconductor Market 11 The Semiconductor Industry’s Growth Rate is Unlikely to Increase 14 Chip Improvements as Moore’s Law Slows 15 Transistor Improvements Continue, but are Slowing 16 Improved Transistor Density Enables Specialization 18 The AI Chip Zoo 19 AI Chip Types 20 AI Chip Benchmarks 22 The Value of State-of-the-Art AI Chips 23 The Efficiency of State-of-the-Art AI Chips Translates into Cost-Effectiveness 23 Compute-Intensive AI Algorithms are Bottlenecked by Chip Costs and Speed 26 U.S. and Chinese AI Chips and Implications for National Competitiveness 27 Appendix A: Basics of Semiconductors and Chips 31 Appendix B: How AI Chips Work 33 Parallel Computing 33 Low-Precision Computing 34 Memory Optimization 35 Domain-Specific Languages 36 Appendix C: AI Chip Benchmarking Studies 37 Appendix D: Chip Economics Model 39 Chip Transistor Density, Design Costs, and Energy Costs 40 Foundry, Assembly, Test and Packaging Costs 41 Acknowledgments 44 Center for Security and Emerging Technology | 2 Introduction and Summary Artificial intelligence will play an important role in national and international security in the years to come. -
ELF Opengo: an Analysis and Open Reimplementation of Alphazero
ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero Yuandong Tian 1 Jerry Ma * 1 Qucheng Gong * 1 Shubho Sengupta * 1 Zhuoyuan Chen 1 James Pinkerton 1 C. Lawrence Zitnick 1 Abstract However, these advances in playing ability come at signifi- The AlphaGo, AlphaGo Zero, and AlphaZero cant computational expense. A single training run requires series of algorithms are remarkable demonstra- millions of selfplay games and days of training on thousands tions of deep reinforcement learning’s capabili- of TPUs, which is an unattainable level of compute for the ties, achieving superhuman performance in the majority of the research community. When combined with complex game of Go with progressively increas- the unavailability of code and models, the result is that the ing autonomy. However, many obstacles remain approach is very difficult, if not impossible, to reproduce, in the understanding of and usability of these study, improve upon, and extend. promising approaches by the research commu- In this paper, we propose ELF OpenGo, an open-source nity. Toward elucidating unresolved mysteries reimplementation of the AlphaZero (Silver et al., 2018) and facilitating future research, we propose ELF algorithm for the game of Go. We then apply ELF OpenGo OpenGo, an open-source reimplementation of the toward the following three additional contributions. AlphaZero algorithm. ELF OpenGo is the first open-source Go AI to convincingly demonstrate First, we train a superhuman model for ELF OpenGo. Af- superhuman performance with a perfect (20:0) ter running our AlphaZero-style training software on 2,000 record against global top professionals. We ap- GPUs for 9 days, our 20-block model has achieved super- ply ELF OpenGo to conduct extensive ablation human performance that is arguably comparable to the 20- studies, and to identify and analyze numerous in- block models described in Silver et al.(2017) and Silver teresting phenomena in both the model training et al.(2018).