45-Year CPU Evolution: One Law and Two Equations
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Caching Basics
Introduction Why memory subsystem design is important • CPU speeds increase 25%-30% per year • DRAM speeds increase 2%-11% per year Autumn 2006 CSE P548 - Memory Hierarchy 1 Memory Hierarchy Levels of memory with different sizes & speeds • close to the CPU: small, fast access • close to memory: large, slow access Memory hierarchies improve performance • caches: demand-driven storage • principal of locality of reference temporal: a referenced word will be referenced again soon spatial: words near a reference word will be referenced soon • speed/size trade-off in technology ⇒ fast access for most references First Cache: IBM 360/85 in the late ‘60s Autumn 2006 CSE P548 - Memory Hierarchy 2 1 Cache Organization Block: • # bytes associated with 1 tag • usually the # bytes transferred on a memory request Set: the blocks that can be accessed with the same index bits Associativity: the number of blocks in a set • direct mapped • set associative • fully associative Size: # bytes of data How do you calculate this? Autumn 2006 CSE P548 - Memory Hierarchy 3 Logical Diagram of a Cache Autumn 2006 CSE P548 - Memory Hierarchy 4 2 Logical Diagram of a Set-associative Cache Autumn 2006 CSE P548 - Memory Hierarchy 5 Accessing a Cache General formulas • number of index bits = log2(cache size / block size) (for a direct mapped cache) • number of index bits = log2(cache size /( block size * associativity)) (for a set-associative cache) Autumn 2006 CSE P548 - Memory Hierarchy 6 3 Design Tradeoffs Cache size the bigger the cache, + the higher the hit ratio -
Intel 8080 Datasheet
infel.. 8080A/8080A-1/8080A-2 8-BIT N-CHANNEL MICROPROCESSOR • TTL Drive Capability • Decimal, Binary, and Double Precision • 2,..,s (-1:1.3,..,s, -2:1.5 ,..,s) Instruction Arithmetic Cycle • Ability to Provide Priority Vectored • Powerful Problem Solving Instruction Interrupts Set • 512 Directly Addressed 110 Ports 6 General Purpose Registers and an Available In EXPRESS • Accumulator • - Standard Temperature Range 16-Blt Program Counter for Directly Available In 4Q-Lead Cerdlp and Plastic • Addressing up to 64K Bytes of Memory • Packages 16-Blt Stack Pointer and Stack (See Packaging Spec. Order #231369) • Manipulation Instructions for Rapid Switching of the Program Environment • The Intel 8080A is a complete 8-bit parallel central processing unit (CPU). It is fabricated on a single LSI chip using Intel's n-channel silicon gate MOS process. This offers the user a high performance solution to control and processing applications. The 8080A contains 6 8-bit general purpose working registers and an accumulator. The 6 general purpose registers may be addressed individually or in pairs providing both single and double precision operators. Arithmetic and logical instructions set or reset 4 testable flags. A fifth flag provides decimal arithmetic opera tion. The 8080A has an external stack feature wherein any portion of memory may be used as a last in/first out stack to store/retrieve the contents of the accumulator, flags, program counter, and all of the 6 general purpose registers. The 16-bit stack pointer controls the addressing of this external stack. This stack gives the 8080A the ability to easily handle multiple level priority interrupts by rapidly storing and restoring processor status. -
Appendix D an Alternative to RISC: the Intel 80X86
D.1 Introduction D-2 D.2 80x86 Registers and Data Addressing Modes D-3 D.3 80x86 Integer Operations D-6 D.4 80x86 Floating-Point Operations D-10 D.5 80x86 Instruction Encoding D-12 D.6 Putting It All Together: Measurements of Instruction Set Usage D-14 D.7 Concluding Remarks D-20 D.8 Historical Perspective and References D-21 D An Alternative to RISC: The Intel 80x86 The x86 isn’t all that complex—it just doesn’t make a lot of sense. Mike Johnson Leader of 80x86 Design at AMD, Microprocessor Report (1994) © 2003 Elsevier Science (USA). All rights reserved. D-2 I Appendix D An Alternative to RISC: The Intel 80x86 D.1 Introduction MIPS was the vision of a single architect. The pieces of this architecture fit nicely together and the whole architecture can be described succinctly. Such is not the case of the 80x86: It is the product of several independent groups who evolved the architecture over 20 years, adding new features to the original instruction set as you might add clothing to a packed bag. Here are important 80x86 milestones: I 1978—The Intel 8086 architecture was announced as an assembly language– compatible extension of the then-successful Intel 8080, an 8-bit microproces- sor. The 8086 is a 16-bit architecture, with all internal registers 16 bits wide. Whereas the 8080 was a straightforward accumulator machine, the 8086 extended the architecture with additional registers. Because nearly every reg- ister has a dedicated use, the 8086 falls somewhere between an accumulator machine and a general-purpose register machine, and can fairly be called an extended accumulator machine. -
The Birth, Evolution and Future of Microprocessor
The Birth, Evolution and Future of Microprocessor Swetha Kogatam Computer Science Department San Jose State University San Jose, CA 95192 408-924-1000 [email protected] ABSTRACT timed sequence through the bus system to output devices such as The world's first microprocessor, the 4004, was co-developed by CRT Screens, networks, or printers. In some cases, the terms Busicom, a Japanese manufacturer of calculators, and Intel, a U.S. 'CPU' and 'microprocessor' are used interchangeably to denote the manufacturer of semiconductors. The basic architecture of 4004 same device. was developed in August 1969; a concrete plan for the 4004 The different ways in which microprocessors are categorized are: system was finalized in December 1969; and the first microprocessor was successfully developed in March 1971. a) CISC (Complex Instruction Set Computers) Microprocessors, which became the "technology to open up a new b) RISC (Reduced Instruction Set Computers) era," brought two outstanding impacts, "power of intelligence" and "power of computing". First, microprocessors opened up a new a) VLIW(Very Long Instruction Word Computers) "era of programming" through replacing with software, the b) Super scalar processors hardwired logic based on IC's of the former "era of logic". At the same time, microprocessors allowed young engineers access to "power of computing" for the creative development of personal 2. BIRTH OF THE MICROPROCESSOR computers and computer games, which in turn led to growth in the In 1970, Intel introduced the first dynamic RAM, which increased software industry, and paved the way to the development of high- IC memory by a factor of four. -
Cuda C Best Practices Guide
CUDA C BEST PRACTICES GUIDE DG-05603-001_v9.0 | June 2018 Design Guide TABLE OF CONTENTS Preface............................................................................................................ vii What Is This Document?..................................................................................... vii Who Should Read This Guide?...............................................................................vii Assess, Parallelize, Optimize, Deploy.....................................................................viii Assess........................................................................................................ viii Parallelize.................................................................................................... ix Optimize...................................................................................................... ix Deploy.........................................................................................................ix Recommendations and Best Practices.......................................................................x Chapter 1. Assessing Your Application.......................................................................1 Chapter 2. Heterogeneous Computing.......................................................................2 2.1. Differences between Host and Device................................................................ 2 2.2. What Runs on a CUDA-Enabled Device?...............................................................3 Chapter 3. Application Profiling............................................................................. -
Hierarchical Roofline Analysis for Gpus: Accelerating Performance
Hierarchical Roofline Analysis for GPUs: Accelerating Performance Optimization for the NERSC-9 Perlmutter System Charlene Yang, Thorsten Kurth Samuel Williams National Energy Research Scientific Computing Center Computational Research Division Lawrence Berkeley National Laboratory Lawrence Berkeley National Laboratory Berkeley, CA 94720, USA Berkeley, CA 94720, USA fcjyang, [email protected] [email protected] Abstract—The Roofline performance model provides an Performance (GFLOP/s) is bound by: intuitive and insightful approach to identifying performance bottlenecks and guiding performance optimization. In prepa- Peak GFLOP/s GFLOP/s ≤ min (1) ration for the next-generation supercomputer Perlmutter at Peak GB/s × Arithmetic Intensity NERSC, this paper presents a methodology to construct a hi- erarchical Roofline on NVIDIA GPUs and extend it to support which produces the traditional Roofline formulation when reduced precision and Tensor Cores. The hierarchical Roofline incorporates L1, L2, device memory and system memory plotted on a log-log plot. bandwidths into one single figure, and it offers more profound Previously, the Roofline model was expanded to support insights into performance analysis than the traditional DRAM- the full memory hierarchy [2], [3] by adding additional band- only Roofline. We use our Roofline methodology to analyze width “ceilings”. Similarly, additional ceilings beneath the three proxy applications: GPP from BerkeleyGW, HPGMG Roofline can be added to represent performance bottlenecks from AMReX, and conv2d from TensorFlow. In so doing, we demonstrate the ability of our methodology to readily arising from lack of vectorization or the failure to exploit understand various aspects of performance and performance fused multiply-add (FMA) instructions. bottlenecks on NVIDIA GPUs and motivate code optimizations. -
An Algorithmic Theory of Caches by Sridhar Ramachandran
An algorithmic theory of caches by Sridhar Ramachandran Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Master of Science at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY. December 1999 Massachusetts Institute of Technology 1999. All rights reserved. Author Department of Electrical Engineering and Computer Science Jan 31, 1999 Certified by / -f Charles E. Leiserson Professor of Computer Science and Engineering Thesis Supervisor Accepted by Arthur C. Smith Chairman, Departmental Committee on Graduate Students MSSACHUSVTS INSTITUT OF TECHNOLOGY MAR 0 4 2000 LIBRARIES 2 An algorithmic theory of caches by Sridhar Ramachandran Submitted to the Department of Electrical Engineeringand Computer Science on Jan 31, 1999 in partialfulfillment of the requirementsfor the degree of Master of Science. Abstract The ideal-cache model, an extension of the RAM model, evaluates the referential locality exhibited by algorithms. The ideal-cache model is characterized by two parameters-the cache size Z, and line length L. As suggested by its name, the ideal-cache model practices automatic, optimal, omniscient replacement algorithm. The performance of an algorithm on the ideal-cache model consists of two measures-the RAM running time, called work complexity, and the number of misses on the ideal cache, called cache complexity. This thesis proposes the ideal-cache model as a "bridging" model for caches in the sense proposed by Valiant [49]. A bridging model for caches serves two purposes. It can be viewed as a hardware "ideal" that influences cache design. On the other hand, it can be used as a powerful tool to design cache-efficient algorithms. -
Generalized Methods for Application Specific Hardware Specialization
Generalized methods for application specific hardware specialization by Snehasish Kumar M. Sc., Simon Fraser University, 2013 B. Tech., Biju Patnaik University of Technology, 2010 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in the School of Computing Science Faculty of Applied Sciences c Snehasish Kumar 2017 SIMON FRASER UNIVERSITY Spring 2017 All rights reserved. However, in accordance with the Copyright Act of Canada, this work may be reproduced without authorization under the conditions for “Fair Dealing.” Therefore, limited reproduction of this work for the purposes of private study, research, education, satire, parody, criticism, review and news reporting is likely to be in accordance with the law, particularly if cited appropriately. Approval Name: Snehasish Kumar Degree: Doctor of Philosophy (Computing Science) Title: Generalized methods for application specific hardware specialization Examining Committee: Chair: Binay Bhattacharyya Professor Arrvindh Shriraman Senior Supervisor Associate Professor Simon Fraser University William Sumner Supervisor Assistant Professor Simon Fraser University Vijayalakshmi Srinivasan Supervisor Research Staff Member, IBM Research Alexandra Fedorova Supervisor Associate Professor University of British Columbia Richard Vaughan Internal Examiner Associate Professor Simon Fraser University Andreas Moshovos External Examiner Professor University of Toronto Date Defended: November 21, 2016 ii Abstract Since the invention of the microprocessor in 1971, the computational capacity of the microprocessor has scaled over 1000× with Moore and Dennard scaling. Dennard scaling ended with a rapid increase in leakage power 30 years after it was proposed. This ushered in the era of multiprocessing where additional transistors afforded by Moore’s scaling were put to use. With the scaling of computational capacity no longer guaranteed every generation, application specific hardware specialization is an attractive alternative to sustain scaling trends. -
14. Caching and Cache-Efficient Algorithms
MITOCW | 14. Caching and Cache-Efficient Algorithms The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high quality educational resources for free. To make a donation or to view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw.mit.edu. JULIAN SHUN: All right. So we've talked a little bit about caching before, but today we're going to talk in much more detail about caching and how to design cache-efficient algorithms. So first, let's look at the caching hardware on modern machines today. So here's what the cache hierarchy looks like for a multicore chip. We have a whole bunch of processors. They all have their own private L1 caches for both the data, as well as the instruction. They also have a private L2 cache. And then they share a last level cache, or L3 cache, which is also called LLC. They're all connected to a memory controller that can access DRAM. And then, oftentimes, you'll have multiple chips on the same server, and these chips would be connected through a network. So here we have a bunch of multicore chips that are connected together. So we can see that there are different levels of memory here. And the sizes of each one of these levels of memory is different. So the sizes tend to go up as you move up the memory hierarchy. The L1 caches tend to be about 32 kilobytes. In fact, these are the specifications for the machines that you're using in this class. -
Professor Won Woo Ro, School of Electrical and Electronic Engineering Yonsei University the Intel® 4004 Microprocessor, Introdu
Professor Won Woo Ro, School of Electrical and Electronic Engineering Yonsei University The 1st Microprocessor The Intel® 4004 microprocessor, introduced in November 1971 An electronics revolution that changed our world. There were no customer‐ programmable microprocessors on the market before the 4004. It propelled software into the limelight as a key player in the world of digital electronics design. 4004 Microprocessor Display at New Intel Museum A Japanese calculator maker (Busicom) asked to design: A set of 12 custom logic chips for a line of programmable calculators. Marcian E. "Ted" Hoff Recognized the integrated circuit technology (of the day) had advanced enough to build a single chip, general purpose computer. Federico Faggin to turn Hoff's vision into a silicon reality. (In less than one year, Faggin and his team delivered the 4004, which was introduced in November, 1971.) The world's first microprocessor application was this Busicom calculator. (sold about 100,000 calculators.) Measuring 1/8 inch wide by 1/6 inch long, consisting of 2,300 transistors, Intel’s 4004 microprocessor had as much computing power as the first electronic computer, ENIAC. 2 inch 4004 and 12 inch Core™2 Duo wafer ENIAC, built in 1946, filled 3000‐cubic‐ feet of space and contained 18,000 vacuum tubes. The 4004 microprocessor could execute 60,000 operations per second Running frequency: 108 KHz Founders wanted to name their new company Moore Noyce. However the name sounds very much similar to “more noise”. "Only the paranoid survive". Moore received a B.S. degree in Chemistry from the University of California, Berkeley in 1950 and a Ph.D. -
High-Performance Decimal Floating-Point Units
UNIVERSIDADE DE SANTIAGO DE COMPOSTELA DEPARTAMENTO DE ELECTRONICA´ E COMPUTACION´ PhD. Dissertation High-Performance Decimal Floating-Point Units Alvaro´ Vazquez´ Alvarez´ Santiago de Compostela, January 2009 To my family A´ mina˜ familia Acknowledgements It has been a long way to see this thesis successfully concluded, at least longer than what I imagined it. Perhaps the moment to thank and acknowledge everyone’s contributions is the most eagerly awaited. This thesis could not have been possible without the support of several people and organizations whose contributions I am very grateful. First of all, I want to express my sincere gratitude to my thesis advisor, Elisardo Antelo. Specially, I would like to emphasize the invaluable support he offered to me all these years. His ideas and contributions have a major influence on this thesis. I would like to thank all people in the Departamento de Electronica´ e Computacion´ for the material and personal help they gave me to carry out this thesis, and for providing a friendly place to work. In particular, I would like to mention to Prof. Javier D. Bruguera and the other staff of the Computer Architecture Group. Many thanks to Paula, David, Pichel, Marcos, Juanjo, Oscar,´ Roberto and my other workmates for their friendship and help. I am very grateful to IBM Germany for their financial support though a one-year research contract. I would like to thank Ralf Fischer, lead of hardware development, and Peter Roth and Stefan Wald, team managers at IBM Deutchland Entwicklung in Boblingen.¨ I would like to extend my gratitude to the FPU design team, in special to Silvia Muller¨ and Michael Kroner,¨ for their help and the warm welcome I received during my stay in Boblingen.¨ I would also like to thank Eric Schwarz from IBM for his support. -
Comparing the Power and Performance of Intel's SCC to State
Comparing the Power and Performance of Intel’s SCC to State-of-the-Art CPUs and GPUs Ehsan Totoni, Babak Behzad, Swapnil Ghike, Josep Torrellas Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA E-mail: ftotoni2, bbehza2, ghike2, [email protected] Abstract—Power dissipation and energy consumption are be- A key architectural challenge now is how to support in- coming increasingly important architectural design constraints in creasing parallelism and scale performance, while being power different types of computers, from embedded systems to large- and energy efficient. There are multiple options on the table, scale supercomputers. To continue the scaling of performance, it is essential that we build parallel processor chips that make the namely “heavy-weight” multi-cores (such as general purpose best use of exponentially increasing numbers of transistors within processors), “light-weight” many-cores (such as Intel’s Single- the power and energy budgets. Intel SCC is an appealing option Chip Cloud Computer (SCC) [1]), low-power processors (such for future many-core architectures. In this paper, we use various as embedded processors), and SIMD-like highly-parallel archi- scalable applications to quantitatively compare and analyze tectures (such as General-Purpose Graphics Processing Units the performance, power consumption and energy efficiency of different cutting-edge platforms that differ in architectural build. (GPGPUs)). These platforms include the Intel Single-Chip Cloud Computer The Intel SCC [1] is a research chip made by Intel Labs (SCC) many-core, the Intel Core i7 general-purpose multi-core, to explore future many-core architectures. It has 48 Pentium the Intel Atom low-power processor, and the Nvidia ION2 (P54C) cores in 24 tiles of two cores each.