PPT-Cache Memory and Performance Many of the following slides are taken with permission

Cache Memory and Performance Many of the following slides are taken with permission from Complete Powerpoint Lecture Notes for Computer Systems A Programmers Perspective CSAPP Randal E Bryant. the Measurement of Memory Systems. Xian-He Sun . Dawei. Wang. November 2011. Memory Wall Problem. . µProc 1.52/yr. .. (2X/1.5yr). Processor-Memory. Performance Gap:. (grows 50% / year). DRAM. 7. : . A column-oriented DBMS. Ryan Johnson. CSC2531. The memory wall has arrived. CPU performance. +70%/year. Memory performance. latency: -50%/. decade. bandwidth: +20%/year (est.). Why?. DRAM focus on capacity (+70%/year). Andrew Putnam, Susan Eggers. Dave Bennett, Eric Dellinger, Jeff Mason, . Henry Styles, . Prasanna. . Sundararajan. , Ralph Wittig. University of . Washington. -- CSE. Xilinx Research Labs. High-Performance Computing. 1. , Jeanine Cook. 2. , . Nafiul. Siddique. 1. 1. New Mexico Sate University. 2. Sandia National Laboratories . Insight into . Application Performance Using. Application-Dependent Characteristics. Introduction. Professor Alvin R. Lebeck. Computer Science 220. Fall . 2008. Admin. Work . on Projects. Read . NUCA paper. Review: ABCs of caches. Associativity. Block size. Capacity. Number of sets S = C/(BA). 1-way (Direct-mapped). Memory Wall . The . growing disparity of speed between CPU and memory outside the CPU . chip. Bandwidth wall: limited . communication bandwidth beyond chip . boundaries. Solution . Memory hierarchy . Why it works: The principle of locality. How it . works. : The architectural details. Von Neumann Architecture. Cost/performance analysis is a constant theme in computer engineering ‒ which is why the proper choice of performance metric is important. Hierarchy with Hi-Spade. . Phillip B. Gibbons. Intel Labs Pittsburgh. September 22, 2011. Abstract. The . goal of the Hi-Spade project is to enable a hierarchy-savvy approach to algorithm design and systems for emerging parallel hierarchies. Good performance often requires effective use of the cache/memory/storage hierarchy of the target computing platform. Two recent trends---pervasive multi-cores and pervasive flash-based SSDs---provide both new challenges and new opportunities for maximizing performance. The project seeks to create abstractions, tools and techniques that (. Lecture for CPSC 5155. Edward Bosworth, Ph.D.. Computer Science Department. Columbus State University. The Simple View of Memory. The simplest view of memory is . that presented . at the ISA (Instruction Set Architecture) level. At this level, memory is a . Direct-mapped caches. Set-associative caches. Impact of caches on performance. CS 105. Tour of the Black Holes of Computing. Cache Memories. C. ache memories . are small, fast SRAM-based memories managed automatically in hardware. Computer Science 252. Spring 2002. CS252. Graduate Computer Architecture. Lecture 1. Introduction. Outline. Why Take CS252?. Fundamental Abstractions & Concepts. Instruction Set Architecture & Organization. 1 Memory & Cache Memories: Review 2 Memory is required for storing Data Instructions Different memory types Dynamic RAM Static RAM Read-only memory (ROM) Characteristics Access time Price Volatility Virtual Memory Use main memory as a “cache” for secondary (disk) storage Managed jointly by CPU hardware and the operating system (OS) Programs share main memory Each gets a private virtual address space holding its frequently used code and data Stephan Meier. Some slides authored by Tyler Huberty, Onur Mutlu (used with permission). Prefetching. Outline. Outline. Motivation. Instruction prefetching. Data prefetching. Research directions. A few definitions before we start….