Advanced Computer Architecture Sub Code 15CS72 Branch: CSE
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Evaluation of Architectural Support for Global Address-Based
Evaluation of Architectural Supp ort for Global AddressBased Communication in LargeScale Parallel Machines y y Arvind Krishnamurthy Klaus E Schauser Chris J Scheiman Randolph Y Wang David E Culler and Katherine Yelick the sp ecic target architecture Wehave develop ed multi Abstract ple highly optimized versions of this compiler employing a Largescale parallel machines are incorp orating increas range of co degeneration strategies for machines with dedi ingly sophisticated architectural supp ort for userlevel mes cated network pro cessors In this studywe use this sp ec saging and global memory access We provide a systematic trum of runtime techniques to evaluate the p erformance evaluation of a broad sp ectrum of current design alternatives tradeos in architectural supp ort for communication found based on our implementations of a global address language in several of the current largescale parallel machines on the Thinking Machines CM Intel Paragon Meiko CS We consider ve imp ortant largescale parallel platforms Cray TD and Berkeley NOW This evaluation includes that havevarying degrees of architectural supp ort for com a range of compilation strategies that makevarying use of munication the Thinking Machines CM Intel Paragon the network pro cessor each is optimized for the target ar Meiko CS Cray TD and Berkeley NOW The CM pro chitecture and the particular strategyWe analyze a family vides direct userlevel access to the network the Paragon of interacting issues that determine the p erformance trade provides a network pro cessor -
Parallel Computer Systems
Parallel Computer Systems Randal E. Bryant CS 347 Lecture 27 April 29, 1997 Topics • Parallel Applications • Shared vs. Distributed Model • Concurrency Models • Single Bus Systems • Network-Based Systems • Lessons Learned Motivation Limits to Sequential Processing • Cannot push clock rates beyond technological limits • Instruction-level parallelism gets diminishing returns – 4-way superscalar machines only get average of 1.5 instructions / cycle – Branch prediction, speculative execution, etc. yield diminishing returns Applications have Insatiable Appetite for Computing • Modeling of physical systems • Virtual reality, real-time graphics, video • Database search, data mining Many Applications can Exploit Parallelism • Work on multiple parts of problem simultaneously • Synchronize to coordinate efforts • Communicate to share information – 2 – CS 347 S’97 Historical Perspective: The Graveyard • Lots of venture capital and DoD Resarch $$’s • Too big to enumerate, but some examples … ILLIAC IV • Early research machine with overambitious technology Thinking Machines • CM-2: 64K single-bit processors with single controller (SIMD) • CM-5: Tightly coupled network of SPARC processors Encore Computer • Shared memory machine using National microprocessors Kendall Square Research KSR-1 • Shared memory machine using proprietary processor NCUBE / Intel Hypercube / Intel Paragon • Connected network of small processors • Survive only in niche markets – 3 – CS 347 S’97 Historical Perspective: Successes Shared Memory Multiprocessors (SMP’s) • E.g., SGI -
Embedded DRAM
Embedded DRAM Raviprasad Kuloor Semiconductor Research and Development Centre, Bangalore IBM Systems and Technology Group DRAM Topics Introduction to memory DRAM basics and bitcell array eDRAM operational details (case study) Noise concerns Wordline driver (WLDRV) and level translators (LT) Challenges in eDRAM Understanding Timing diagram – An example References Slide 1 Acknowledgement • John Barth, IBM SRDC for most of the slides content • Madabusi Govindarajan • Subramanian S. Iyer • Many Others Slide 2 Topics Introduction to memory DRAM basics and bitcell array eDRAM operational details (case study) Noise concerns Wordline driver (WLDRV) and level translators (LT) Challenges in eDRAM Understanding Timing diagram – An example Slide 3 Memory Classification revisited Slide 4 Motivation for a memory hierarchy – infinite memory Memory store Processor Infinitely fast Infinitely large Cycles per Instruction Number of processor clock cycles (CPI) = required per instruction CPI[ ∞ cache] Finite memory speed Memory store Processor Finite speed Infinite size CPI = CPI[∞ cache] + FCP Finite cache penalty Locality of reference – spatial and temporal Temporal If you access something now you’ll need it again soon e.g: Loops Spatial If you accessed something you’ll also need its neighbor e.g: Arrays Exploit this to divide memory into hierarchy Hit L2 L1 (Slow) Processor Miss (Fast) Hit Register Cache size impacts cycles-per-instruction Access rate reduces Slower memory is sufficient Cache size impacts cycles-per-instruction For a 5GHz -
Chapter 6 : Memory System
Computer Organization and Architecture Chapter 6 : Memory System Chapter – 6 Memory System 6.1 Microcomputer Memory Memory is an essential component of the microcomputer system. It stores binary instructions and datum for the microcomputer. The memory is the place where the computer holds current programs and data that are in use. None technology is optimal in satisfying the memory requirements for a computer system. Computer memory exhibits perhaps the widest range of type, technology, organization, performance and cost of any feature of a computer system. The memory unit that communicates directly with the CPU is called main memory. Devices that provide backup storage are called auxiliary memory or secondary memory. 6.2 Characteristics of memory systems The memory system can be characterised with their Location, Capacity, Unit of transfer, Access method, Performance, Physical type, Physical characteristics, Organisation. Location • Processor memory: The memory like registers is included within the processor and termed as processor memory. • Internal memory: It is often termed as main memory and resides within the CPU. • External memory: It consists of peripheral storage devices such as disk and magnetic tape that are accessible to processor via i/o controllers. Capacity • Word size: Capacity is expressed in terms of words or bytes. — The natural unit of organisation • Number of words: Common word lengths are 8, 16, 32 bits etc. — or Bytes Unit of Transfer • Internal: For internal memory, the unit of transfer is equal to the number of data lines into and out of the memory module. • External: For external memory, they are transferred in block which is larger than a word. -
Memory Systems
Memory Systems Patterson & Hennessey Chapter 5 1 Memory Hierarchy Primary Memory Secondary Secondary CPU Cache Main Memory Memory ---- Memory Memory (Disk) (Archival) Registers MC MM MD MA Memory Content: MC ⊆ MM ⊆ MD ⊆ MA Memory Parameters: Other Factors: •Access Time: increase with distance from CPU • Energy consumption •Cost/Bit: decrease with distance from CPU • Reliability •Capacity: increase with distance from CPU • Size/density 2 § 5.1 Introducion Memory Technology Static RAM (SRAM) 0.5ns – 2.5ns, $2000 – $5000 per GB Memory Hierarchy Dynamic RAM (DRAM) CPU 50ns – 70ns, $20 – $75 per GB Increasing distance Level 1 from the CPU in Magnetic disk access time Levels in the Level 2 5ms – 20ms, $0.20 – $2 per GB memory hierarchy Ideal memory Level n Access time of SRAM Capacity and cost/GB of disk Size of the memory at each level 3 Locality of Reference Programs access a small proportion of their address space at any time If an item is referenced: - temporal locality: it will tend to be referenced again soon - spatial locality: nearby items will tend to be referenced soon Why does code have locality? Our initial focus: two levels (upper, lower) block: minimum unit of data transferred hit: data requested is in the upper level miss: data requested is not in the upper level 4 Taking Advantage of Locality Memory hierarchy Store everything on disk Copy recently accessed (and nearby) items from disk to smaller DRAM memory Main memory Copy more recently accessed (and nearby) items from DRAM to smaller/faster SRAM memory -
Scalability Study of KSR-1
Scalability Study of the KSR-1 Appeared in Parallel Computing, Vol 22, 1996, 739-759 Umakishore Ramachandran Gautam Shah S. Ravikumar Jeyakumar Muthukumarasamy College of Computing Georgia Institute of Technology Atlanta, GA 30332 Phone: (404) 894-5136 e-mail: [email protected] Abstract Scalability of parallel architectures is an interesting area of current research. Shared memory parallel programming is attractive stemming from its relative ease in transitioning from sequential programming. However, there has been concern in the architectural community regarding the scalability of shared memory parallel architectures owing to the potential for large latencies for remote memory accesses. KSR-1 is a commercial shared memory parallel architecture, and the scalability of KSR-1 is the focus of this research. The study is conducted using a range of experiments spanning latency measurements, synchronization, and analysis of parallel algorithms for three computational kernels and an application. The key conclusions from this study are as follows: The communication network of KSR-1, a pipelined unidirectional ring, is fairly resilient in supporting simultaneous remote memory accesses from several processors. The multiple communication paths realized through this pipelining help in the ef®cient implementation of tournament-style barrier synchronization algorithms. Parallel algorithms that have fairly regular and contiguous data access patterns scale well on this architecture. The architectural features of KSR-1 such as the poststore and prefetch are useful for boosting the performance of parallel applications. The sizes of the caches available at each node may be too small for ef®ciently implementing large data structures. The network does saturate when there are simultaneous remote memory accesses from a fully populated (32 node) ring. -
A Novel Cache Architecture for Multicore Processor Using Vlsi Sujitha .S, Anitha .N
International Journal of Scientific & Engineering Research, Volume 4, Issue 6, June-2013 178 ISSN 2229-5518 A Novel Cache Architecture For Multicore Processor Using Vlsi Sujitha .S, Anitha .N Abstract— Memory cell design for cache memories is a major concern in current Microprocessors mainly due to its impact on energy consumption, area and access time. Cache memories dissipate an important amount of the energy budget in current microprocessors. An n-bit cache cell, namely macrocell, has been proposed. This cell combines SRAM and eDRAM technologies with the aim of reducing energy consumption while maintaining the performance. The proposed M-Cache is implemented in processor architecture and its performance is evaluated. The main objective of this project is to design a newly modified cache of Macrocell (4T cell). The speed of these cells is comparable to the speed of 6T SRAM cells. The performance of the processor architecture including this modified-cache is better than the conventional Macrocell cache design. Further this Modified Macrocell is implemented in Mobile processor architecture and compared with the conventional memory. Also a newly designed Femtocell is compared with both the macrocell and Modified m-cache cell. Index Terms— Femtocell. L1 cache, L2 cache, Macrocell, Modified M-cell, UART. —————————— —————————— 1 INTRODUCTION The cache operation is that when the CPU requests contents of memory location, it first checks cache for this data, if present, get from cache (fast), if not present, read PROCESSORS are generally able to perform operations required block from main memory to cache. Then, deliver from cache to CPU. Cache includes tags to identify which on operands faster than the access time of large capacity block of main memory is in each cache slot. -
Memory Hierarchy Summary
Carnegie Mellon Carnegie Mellon Today DRAM as building block for main memory The Memory Hierarchy Locality of reference Caching in the memory hierarchy Storage technologies and trends 15‐213 / 18‐213: Introduction to Computer Systems 10th Lecture, Feb 14, 2013 Instructors: Seth Copen Goldstein, Anthony Rowe, Greg Kesden 1 2 Carnegie Mellon Carnegie Mellon Byte‐Oriented Memory Organization Simple Memory Addressing Modes Normal (R) Mem[Reg[R]] • • • . Register R specifies memory address . Aha! Pointer dereferencing in C Programs refer to data by address movl (%ecx),%eax . Conceptually, envision it as a very large array of bytes . In reality, it’s not, but can think of it that way . An address is like an index into that array Displacement D(R) Mem[Reg[R]+D] . and, a pointer variable stores an address . Register R specifies start of memory region . Constant displacement D specifies offset Note: system provides private address spaces to each “process” . Think of a process as a program being executed movl 8(%ebp),%edx . So, a program can clobber its own data, but not that of others nd th From 2 lecture 3 From 5 lecture 4 Carnegie Mellon Carnegie Mellon Traditional Bus Structure Connecting Memory Read Transaction (1) CPU and Memory CPU places address A on the memory bus. A bus is a collection of parallel wires that carry address, data, and control signals. Buses are typically shared by multiple devices. Register file Load operation: movl A, %eax ALU %eax CPU chip Main memory Register file I/O bridge A 0 Bus interface ALU x A System bus Memory bus I/O Main Bus interface bridge memory 5 6 Carnegie Mellon Carnegie Mellon Memory Read Transaction (2) Memory Read Transaction (3) Main memory reads A from the memory bus, retrieves CPU read word xfrom the bus and copies it into register word x, and places it on the bus. -
Problem 1 : Locality of Reference. [10 Points] Problem 2 : Virtual Memory
CMU 18–746 Storage Systems Spring 2010 Solutions to Homework 0 Problem 1 : Locality of reference. [10 points] Spatial locality is the concept that objects logically “close” in location to an object in the cache are more likely to be accessed than objects logically “far.” For example, if disk block #3 is read and placed in the cache, it is more likely that disk block #4 will be referenced soon than block #31,415,926 because blocks #3 and #4 are close to each other on the disk. Temporal locality is the concept that items more recently accessed in the cache are more likely to be accessed than older, “stale” blocks. For example, if disk blocks are read and placed in the cache in the order #271, #828, #182, then it is more likely that disk block #182 will be referenced again soon than block #271. Another way of thinking of this is that a block is likely to be accessed multiple times in a short span of time. Problem 2 : Virtual memory. [20 points] (a) Swapping is the process of evicting a virtual memory page from physical memory (writing the contents of the page to a backing store [for example, a disk]) and replacing the freed memory with another page (either a newly created page, or an existing page read from the backing store). In essence, one virtual memory page is “swapped” with another. Swapping is initiated whenever all physical page frames are allocated and a virtual memory reference is made to a page that’s not in memory. -
The KSR1: Experimentation and Modeling of Poststore Amy Apon Clemson University, [email protected]
Clemson University TigerPrints Publications School of Computing 2-1993 The KSR1: Experimentation and Modeling of Poststore Amy Apon Clemson University, [email protected] E Rosti Universita degli studi de Milano E Smirni Vanderbilt University T D. Wagner Vanderbilt University M Madhukar Vanderbilt University See next page for additional authors Follow this and additional works at: https://tigerprints.clemson.edu/computing_pubs Part of the Computer Sciences Commons Recommended Citation Please use publisher's recommended citation. This Article is brought to you for free and open access by the School of Computing at TigerPrints. It has been accepted for inclusion in Publications by an authorized administrator of TigerPrints. For more information, please contact [email protected]. Authors Amy Apon, E Rosti, E Smirni, T D. Wagner, M Madhukar, and L W. Dowdy This article is available at TigerPrints: https://tigerprints.clemson.edu/computing_pubs/9 3 445b 0374303 7 E. Rasti E. Smirni A. W. Apoa L. w. Dowdy .- .. , . - . .. .. ... ..... i- ORNL/TM- 1228 7 I' Engineering Physics and Mathematics Division ; ?J -2 c_ Mathematical Sciences Section I.' THE KSR1: EXPERIMENTATION AND MODELING OF POSTSTORE E. Rosti E. Smirni t T. D. Wagner + A. W. Apon L. W. Dowdy Dipartimento di Scienze dell'Informazione Universitb degli Studi di Milano Via Comelico 39 20135 Milano, Italy t Computer Science Department Vaiiderbilt University Box 1679, Station B Nashville, TN 37235 Date Published: February 1993 This work was partially supported by sub-contract 19X-SL131V from the Oak Ridge National Laboratory, and by grant N. 92.01615.PF69 from the Italian CNR "Progetto Finalizzato Sistemi Informatici e Calcolo Parallel0 - Sottoprogetto 3." Prepared by the Oak Ridge National Laboratory Oak Ridge, Tennessee 37831 managed by Martin Marietta Energy Systems, Inc. -
Week 6: Assignment Solutions
Week 6: Assignment Solutions 1. Which of the following is true for a memory hierarchy? a. It tries to bridge the processor-memory speed gap. b. The speed of the memory level closest to the processor has the highest speed. c. The capacity of the memory level farthest away from the processor is the largest. d. It is based on the principle of locality of reference. All the answers are true. This follows from the basic definition of memory hierarchy design. The fastest and smallest memory module is closest to the processor. The overall access time of the memory system is close to the access time of the fastest memory level. This is achieved by exploiting locality of reference. 2. Which of the following statements are false? a. Temporal locality arises because of loops in a program. b. Spatial locality arises because of loops in a program. c. Temporal locality arises because of sequential instruction execution. d. Spatial locality arises because of sequential instruction execution. Correct answers are (b) and (c). Temporal locality says that an accessed word will be accessed again in the near future, and this is due to the presence of loops. Spatial locality says that if a work is accessed, then words in the neighborhood will also be accessed in the near future. This happens due to sequential program execution. 3. Assume that a read request takes 50 nsec on a cache miss and 5 nsec on a cache hit. While running a program, it is observed that 80% of the processor’s read requests result in a cache hit. -
Slap: a Split Latency Adaptive Vliw Pipeline Architecture Which Enables On-The-Fly Variable Simd Vector-Length
© 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. arXiv:2102.13301v1 [cs.AR] 26 Feb 2021 SLAP: A SPLIT LATENCY ADAPTIVE VLIW PIPELINE ARCHITECTURE WHICH ENABLES ON-THE-FLY VARIABLE SIMD VECTOR-LENGTH Ashish Shrivastava?y, Alan Gatherer?z, Tong Sunk, Sushma Wokhluk, Alex Chandrak ?Wireless Access Lab, Futurewei Technologies Inc ySenior Member, IEEE zFellow, IEEE ABSTRACT Over the last decade the relative latency of access to shared memory by multicore increased as wire resistance domi- nated latency and low wire density layout pushed multi-port memories farther away from their ports. Various techniques were deployed to improve average memory access latencies, such as speculative pre-fetching and branch-prediction, often leading to high variance in execution time which is unac- ceptable in real-time systems. Smart DMAs can be used to directly copy data into a layer-1 SRAM, but with over- head. The VLIW architecture, the de-facto signal-processing engine, suffers badly from a breakdown in lock-step exe- cution of scalar and vector instructions. We describe the Split Latency Adaptive Pipeline (SLAP) VLIW architecture, a cache performance improvement technology that requires zero change to object code, while removing smart DMAs and their overhead. SLAP builds on the Decoupled Access and Execute concept by 1) breaking lock-step execution of func- tional units, 2) enabling variable vector length for variable data-level parallelism, and 3) adding a novel triangular-load mechanism.