Comparing Cache Architectures and Coherency Protocols on X86-64 Multicore SMP Systems

Total Page:16

File Type:pdf, Size:1020Kb

Comparing Cache Architectures and Coherency Protocols on X86-64 Multicore SMP Systems Comparing Cache Architectures and Coherency Protocols on x86-64 Multicore SMP Systems Daniel Hackenberg Daniel Molka Wolfgang E. Nagel Center for Information Services and High Performance Computing (ZIH) Technische Universität Dresden, 01062 Dresden, Germany {daniel.hackenberg, daniel.molka, wolfgang.nagel}@tu-dresden.de ABSTRACT quad-core designs for dual-socket systems. They integrate Across a broad range of applications, multicore technol- on-chip memory controllers that reduce the memory latency ogy is the most important factor that drives today's mi- and allow the memory bandwidth to scale with the processor croprocessor performance improvements. Closely coupled is count in an SMP system. Serial point-to-point links provide a growing complexity of the memory subsystems with sev- cache-coherent inter-processor connections, thus creating a eral cache levels that need to be exploited efficiently to gain cache-coherent non-uniform memory access (ccNUMA) ar- optimal application performance. Many important imple- chitecture. Both processor families feature a three level mentation details of these memory subsystems are undocu- cache hierarchy and shared last level caches. mented. We therefore present a set of sophisticated bench- The evolution of processor technology towards multicore marks for latency and bandwidth measurements to arbitrary designs drives software technology as well. Parallel program- locations in the memory subsystem. We consider the co- ming paradigms become increasingly attractive to leverage herency state of cache lines to analyze the cache coherency all available resources. This development goes along with a protocols and their performance impact. The potential of change of memory access patterns: While independent pro- our approach is demonstrated with an in-depth compari- cesses typically work on disjoint memory, parallel applica- son of ccNUMA multiprocessor systems with AMD (Shang- tions often use multiple threads to work on a shared data set. hai) and Intel (Nehalem-EP) quad-core x86-64 processors Such parallelism, e.g. producer-consumer problems as well that both feature integrated memory controllers and coher- as synchronization directives inherently require data move- ent point-to-point interconnects. Using our benchmarks we ment between processor cores that is preferably kept on- present fundamental memory performance data and archi- chip. Common memory benchmarks such as STREAM [11] tectural properties of both processors. Our comparison re- are no longer sufficient to adequately assess the memory per- veals in detail how the microarchitectural differences tremen- formance of state-of-the-art processors as they do not cover dously affect the performance of the memory subsystem. important aspects of multicore architectures such as band- width and latency between different processor cores. To improve this situation, we present a set of benchmarks Categories and Subject Descriptors that can be used to determine performance characteristics of C.4 [Performance of Systems]: Measurement techniques memory accesses to arbitrary locations in multicore, multi- processor ccNUMA systems. This includes on- and off-chip General Terms cache-to-cache transfers that we consider to be of growing importance. We also analyze the influence of the cache co- Performance Measurement herency protocol by controlling the coherency state of the cache lines that are accessed by our benchmarks. Keywords Based on these benchmarks we compare the performance Nehalem, Shanghai, Multi-core, Coherency, Benchmark of the memory subsystem in two socket SMP systems with AMD Shanghai and Intel Nehalem processors. Both proces- sors strongly differ in the design of their cache architecture, 1. INTRODUCTION e.g. the last level cache implementation (non-inclusive vs. Multicore technology is established in desktop and server inclusive) and the coherency protocol (MOESI vs. MESIF). systems as well as in high performance computing. The Our results demonstrate how these differences affect the AMD Opteron 2300 family (Shanghai/Barcelona) and In- memory latency and bandwidth. We reveal undocumented tel's Xeon 5500 processors (Nehalem-EP) are native x86-64 architectural properties and memory performance charac- teristics of both processors. While a high-level analysis of application performance is beyond the scope of this paper, Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are we present fundamental data to facilitate such research. not made or distributed for profit or commercial advantage and that copies The paper is organized as follows: Section 2 outlines char- bear this notice and the full citation on the first page. To copy otherwise, to acteristics of our test systems. Section 3 introduces the republish, to post on servers or to redistribute to lists, requires prior specific benchmarks that we use to generate the results in Sections 4 permission and/or a fee. and 5. Section 6 presents related work. Section 7 concludes MICRO’09, December 12–16, 2009, New York, NY, USA. this paper and sketches future work. Copyright 2009 ACM 978-1-60558-798-1/09/12 ...$10.00. 413 Shanghai Shanghai Nehalem-EP Nehalem-EP Core 0 Core 1 Core 2 Core 3 Core 4 Core 5 Core 6 Core 7 Core 0 Core 1 Core 2 Core 3 Core 4 Core 5 Core 6 Core 7 L1 L1 L1 L1 L1 L1 L1 L1 L1 L1 L1 L1 L1 L1 L1 L1 L2 L2 L2 L2 L2 L2 L2 L2 L2 L2 L2 L2 L2 L2 L2 L2 Shared L3 Cache (non-inclusive) Shared L3 Cache (non-inclusive) Shared L3 Cache (inclusive) Shared L3 Cache (inclusive) IMC HT HT IMC IMC QPI QPI IMC F A B E A B C D C D 3 3 3 3 2 2 3 3 I/O I/O 2 2 I/O I/O R R R R R R R R R R D D D D D D D D D D D D D D D D D D D D Figure 1: Block diagram of the AMD (left) and Intel (right) system architecture 2. BACKGROUND AND TEST SYSTEMS AMD's last level cache is non-inclusive [6], i.e neither ex- Dual-socket SMP systems based on AMD Opteron 23** clusive nor inclusive. If a cache line is transferred from the (Shanghai) and Intel Xeon 55** (Nehalem-EP) processors L3 cache into the L1 of any core the line can be removed from have a similar high level design as depicted in Figure 1. the L3. According to AMD this happens if it is \likely" [3] The L1 and L2 caches are implemented per core, while the (further details are undisclosed) that the line is only used L3 cache is shared among all cores of one processor. The by one core, otherwise a copy can be kept in the L3. Both serial point-to-point links HyperTransport (HT) and Quick processors use extended versions of the well-known MESI [7] Path Interconnect (QPI) are used for inter-processor and protocol to ensure cache coherency. AMD Opteron proces- chipset communication. Moreover, each processor contains sors implement the MOESI protocol [2, 5]. The additional its own integrated memory controller (IMC). state owned (O) allows to share modified data without a Although the number of cores, clockrates, and cache sizes write-back to main memory. Nehalem processors implement are similar, benchmark results can differ significantly. For the MESIF protocol [9] and use the forward (F) state to en- example in SPEC's CPU2006 benchmark, the Nehalem typ- sure that shared unmodified data is forwarded only once. ically outperforms AMD's Shanghai [1]. This is a result The configuration of both test systems is detailed in Ta- of multiple aspects such as different instruction-level par- ble 1. The listing shows a major disparity with respect to allelism, Simultaneous Multi-Threading (SMT), and Intel's the main memory configuration. We can assume that Ne- Turbo Boost Technology. Another important factor is the halem's three DDR3-1333 channels outperform Shanghai's architecture of the memory subsystem in conjunction with two DDR2-667 channels (DDR2-800 is supported by the the cache coherency protocol [12]. CPU but not by our test system). However, the main mem- While the basic memory hierarchy structure is similar for ory performance of AMD processors will improve by switch- Nehalem and Shanghai systems, the implementation details ing to new sockets with more memory channels and DDR3. differ significantly. Intel implements an inclusive last level We disable Turbo Boost in our Intel test system as it in- cache in order to filter unnecessary snoop traffic. Core valid troduces result perturbations that are often unpredictable. bits within the L3 cache indicate that a cache line may be Our benchmarks require only one thread per core to access present in a certain core. If a bit is not set, the associated all caches and we therefore disable the potentially disadvan- core certainly does not hold a copy of the cache line, thus tageous SMT feature. We disable the hardware prefetchers reducing snoop traffic to that core. However, unmodified for all latency measurements as they introduce result varia- cache lines may be evicted from a core's cache without noti- tions that distract from the actual hardware properties. The fication of the L3 cache. Therefore, a set core valid bit does bandwidth measurements are more robust and we enable the not guarantee a cache line's presence in a higher level cache. hardware prefetchers unless noted otherwise. Test system Sun Fire X4140 Intel Evaluation Platform Processors 2x AMD Opteron 2384 2x Intel Xeon X5570 Codename Shanghai Nehalem-EP Core/Uncore frequency 2.7 GHz / 2.2 GHz 2.933 GHz / 2.666 GHz Processor Interconnect HyperTransport, 8 GB/s QuickPath Interconnect, 25.6 GB/s Cache line size 64 Bytes L1 cache 64 KiB/64 KiB (per core) 32 KiB/32 KiB (per core) L2 cache 512 KiB (per core), exclusive of L1 256 KiB (per core), non-inclusive L3 cache 6 MiB (shared), non-inclusive 8 MiB (shared), inclusive of L1 and L2 Cache coherency protocol MOESI MESIF Integrated memory controller yes, 2 channel yes, 3 channel 8 x 4 GiB DDR2-667, registered, ECC 6 x 2 GiB DDR3-1333, registered, ECC Main memory (4 DIMMS per processor) (3 DIMMS per processor) Operating system Debian 5.0, Kernel 2.6.28.1 Table 1: Configuration of the test systems 414 3.
Recommended publications
  • 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
    [Show full text]
  • 45-Year CPU Evolution: One Law and Two Equations
    45-year CPU evolution: one law and two equations Daniel Etiemble LRI-CNRS University Paris Sud Orsay, France [email protected] Abstract— Moore’s law and two equations allow to explain the a) IC is the instruction count. main trends of CPU evolution since MOS technologies have been b) CPI is the clock cycles per instruction and IPC = 1/CPI is the used to implement microprocessors. Instruction count per clock cycle. c) Tc is the clock cycle time and F=1/Tc is the clock frequency. Keywords—Moore’s law, execution time, CM0S power dissipation. The Power dissipation of CMOS circuits is the second I. INTRODUCTION equation (2). CMOS power dissipation is decomposed into static and dynamic powers. For dynamic power, Vdd is the power A new era started when MOS technologies were used to supply, F is the clock frequency, ΣCi is the sum of gate and build microprocessors. After pMOS (Intel 4004 in 1971) and interconnection capacitances and α is the average percentage of nMOS (Intel 8080 in 1974), CMOS became quickly the leading switching capacitances: α is the activity factor of the overall technology, used by Intel since 1985 with 80386 CPU. circuit MOS technologies obey an empirical law, stated in 1965 and 2 Pd = Pdstatic + α x ΣCi x Vdd x F (2) known as Moore’s law: the number of transistors integrated on a chip doubles every N months. Fig. 1 presents the evolution for II. CONSEQUENCES OF MOORE LAW DRAM memories, processors (MPU) and three types of read- only memories [1]. The growth rate decreases with years, from A.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • Open Jishen-Zhao-Dissertation.Pdf
    The Pennsylvania State University The Graduate School RETHINKING THE MEMORY HIERARCHY DESIGN WITH NONVOLATILE MEMORY TECHNOLOGIES A Dissertation in Computer Science and Engineering by Jishen Zhao c 2014 Jishen Zhao Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy May 2014 The dissertation of Jishen Zhao was reviewed and approved∗ by the following: Yuan Xie Professor of Computer Science and Engineering Dissertation Advisor, Chair of Committee Mary Jane Irwin Professor of Computer Science and Engineering Vijaykrishnan Narayanan Professor of Computer Science and Engineering Zhiwen Liu Associate Professor of Electrical Engineering Onur Mutlu Associate Professor of Electrical and Computer Engineering Carnegie Mellon University Special Member Lee Coraor Associate Professor of Computer Science and Engineering Director of Academic Affairs ∗Signatures are on file in the Graduate School. Abstract The memory hierarchy, including processor caches and the main memory, is an important component of various computer systems. The memory hierarchy is becoming a fundamental performance and energy bottleneck, due to the widening gap between the increasing bandwidth and energy demands of modern applications and the limited performance and energy efficiency provided by traditional memory technologies. As a result, computer architects are facing significant challenges in developing high-performance, energy-efficient, and reliable memory hierarchies. New byte-addressable nonvolatile memories (NVRAMs) are emerging with unique properties that are likely to open doors to novel memory hierarchy designs to tackle the challenges. However, substantial advancements in redesigning the existing memory hierarchy organizations are needed to realize their full potential. This dissertation focuses on re-architecting the current memory hierarchy design with NVRAMs, producing high-performance, energy-efficient memory designs for both CPU and graphics processor (GPU) systems.
    [Show full text]
  • Beating In-Order Stalls with “Flea-Flicker” Two-Pass Pipelining
    Beating in-order stalls with “flea-flicker”∗two-pass pipelining Ronald D. Barnes Erik M. Nystrom John W. Sias Sanjay J. Patel Nacho Navarro Wen-mei W. Hwu Center for Reliable and High-Performance Computing Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign {rdbarnes, nystrom, sias, sjp, nacho, hwu}@crhc.uiuc.edu Abstract control speculation features allow the compiler to miti- gate control dependences, further increasing static schedul- Accommodating the uncertain latency of load instructions ing freedom. Predication enables the compiler to optimize is one of the most vexing problems in in-order microarchi- program decision and to overlap independent control con- tecture design and compiler development. Compilers can structs while minimizing code growth. In the absence of generate schedules with a high degree of instruction-level unanticipated run-time delays such as cache miss-induced parallelism but cannot effectively accommodate unantici- stalls, the compiler can effectively utilize execution re- pated latencies; incorporating traditional out-of-order exe- sources, overlap execution latencies, and work around exe- cution into the microarchitecture hides some of this latency cution constraints [1]. For example, we have measured that, but redundantly performs work done by the compiler and when run-time stall cycles are discounted, the Intel refer- adds additional pipeline stages. Although effective tech- ence compiler can achieve an average throughput of 2.5 in- niques, such as prefetching and threading, have been pro- structions per cycle (IPC) across SPECint2000 benchmarks posed to deal with anticipable, long-latency misses, the for a 1.0GHz Itanium 2 processor. shorter, more diffuse stalls due to difficult-to-anticipate, Run-time stall cycles of various types prolong the execu- first- or second-level misses are less easily hidden on in- tion of the compiler-generated schedule, in the noted exam- order architectures.
    [Show full text]
  • Performance Analysis of Complex Shared Memory Systems Abridged Version of Dissertation
    Performance Analysis of Complex Shared Memory Systems Abridged Version of Dissertation Daniel Molka September 12, 2016 The goal of this thesis is to improve the understanding of the achieved application performance on existing hardware. It can be observed that the scaling of parallel applications on multi-core processors differs significantly from the scaling on multiple processors. Therefore, the properties of shared resources in contemporary multi-core processors as well as remote accesses in multi-processor systems are investigated and their respective impact on the application performance is analyzed. As a first step, a comprehensive suite of highly optimized micro-benchmarks is developed. These benchmarks are able to determine the performance of memory accesses depending on the location and coherence state of the data. They are used to perform an in-depth analysis of the characteristics of memory accesses in contemporary multi-processor systems, which identifies potential bottlenecks. In order to localize performance problems, it also has to be determined to which extend the application performance is limited by certain resources. Therefore, a methodology to derive metrics for the utilization of individual components in the memory hierarchy as well as waiting times caused by memory accesses is developed in the second step. The approach is based on hardware performance counters. The developed micro-benchmarks are used to selectively stress individual components, which can be used to identify the events that provide a reasonable assessment for the utilization of the respective component and the amount of time that is spent waiting for memory accesses to complete. Finally, the knowledge gained from this process is used to implement a visualization of memory related performance issues in existing performance analysis tools.
    [Show full text]
  • Chapter 2: Memory Hierarchy Design
    Computer Architecture A Quantitative Approach, Fifth Edition Chapter 2 Memory Hierarchy Design Copyright © 2012, Elsevier Inc. All rights reserved. 1 Contents 1. Memory hierarchy 1. Basic concepts 2. Design techniques 2. Caches 1. Types of caches: Fully associative, Direct mapped, Set associative 2. Ten optimization techniques 3. Main memory 1. Memory technology 2. Memory optimization 3. Power consumption 4. Memory hierarchy case studies: Opteron, Pentium, i7. 5. Virtual memory 6. Problem solving dcm 2 Introduction Introduction Programmers want very large memory with low latency Fast memory technology is more expensive per bit than slower memory Solution: organize memory system into a hierarchy Entire addressable memory space available in largest, slowest memory Incrementally smaller and faster memories, each containing a subset of the memory below it, proceed in steps up toward the processor Temporal and spatial locality insures that nearly all references can be found in smaller memories Gives the allusion of a large, fast memory being presented to the processor Copyright © 2012, Elsevier Inc. All rights reserved. 3 Memory hierarchy Processor L1 Cache L2 Cache L3 Cache Main Memory Latency Hard Drive or Flash Capacity (KB, MB, GB, TB) Copyright © 2012, Elsevier Inc. All rights reserved. 4 PROCESSOR L1: I-Cache D-Cache I-Cache instruction cache D-Cache data cache U-Cache unified cache L2: U-Cache Different functional units fetch information from I-cache and D-cache: decoder and L3: U-Cache scheduler operate with I- cache, but integer execution unit and floating-point unit Main: Main Memory communicate with D-cache. Copyright © 2012, Elsevier Inc. All rights reserved.
    [Show full text]
  • Architecting and Programming an Incoherent Multiprocessor Cache
    © 2015 Wooil Kim ARCHITECTING, PROGRAMMING, AND EVALUATING AN ON-CHIP INCOHERENT MULTI-PROCESSOR MEMORY HIERARCHY BY WOOIL KIM DISSERTATION Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science in the Graduate College of the University of Illinois at Urbana-Champaign, 2015 Urbana, Illinois Doctoral Committee: Professor Josep Torrellas, Chair and Director of Research Professor Marc Snir Professor David Padua Professor William Gropp Professor P. Sadayappan, Ohio State University Abstract New architectures for extreme-scale computing need to be designed for higher energy efficiency than current systems. The DOE-funded Traleika Glacier architecture is a recently-proposed extreme-scale manycore that radically simplifies the architecture, and proposes a cluster-based on-chip memory hierarchy without hardware cache coherence. Programming for such an environment, which can use scratchpads or incoherent caches, is challenging. Hence, this thesis focuses on architecting, programming, and evaluating an on-chip incoherent multiprocessor memory hierarchy. This thesis starts by examining incoherent multiprocessor caches. It proposes ISA support for data movement in such an environment, and two relatively user-friendly programming approaches that use the ISA. The ISA support is largely based on writeback and self-invalidation instructions, while the program- ming approaches involve shared-memory programming either inside a cluster only, or across clusters. The thesis also includes compiler transformations for such an incoherent cache hierarchy. Our simulation results show that, with our approach, the execution of applications on incoherent cache hierarchies can deliver reasonable performance. For execution within a cluster, the average execution time of our applications is only 2% higher than with hardware cache coherence.
    [Show full text]
  • 55 Measuring Microarchitectural Details of Multi- and Many-Core
    55 Measuring Microarchitectural Details of Multi- and Many-Core Memory Systems through Microbenchmarking ZHENMAN FANG, University of California Los Angeles SANYAM MEHTA, PEN-CHUNG YEW and ANTONIA ZHAI, University of Minnesota JAMES GREENSKY and GAUTHAM BEERAKA,Intel BINYU ZANG, Shanghai Jiao Tong University As multicore and many-core architectures evolve, their memory systems are becoming increasingly more complex. To bridge the latency and bandwidth gap between the processor and memory, they often use a mix of multilevel private/shared caches that are either blocking or nonblocking and are connected by high-speed network-on-chip. Moreover, they also incorporate hardware and software prefetching and simultaneous mul- tithreading (SMT) to hide memory latency. On such multi- and many-core systems, to incorporate various memory optimization schemes using compiler optimizations and performance tuning techniques, it is cru- cial to have microarchitectural details of the target memory system. Unfortunately, such details are often unavailable from vendors, especially for newly released processors. In this article, we propose a novel microbenchmarking methodology based on short elapsed-time events (SETEs) to obtain comprehensive memory microarchitectural details in multi- and many-core processors. This approach requires detailed analysis of potential interfering factors that could affect the intended behavior of such memory systems. We lay out effective guidelines to control and mitigate those interfering factors. Taking the impact of SMT into consideration, our proposed methodology not only can measure traditional cache/memory latency and off-chip bandwidth but also can uncover the details of software and hardware prefetching units not attempted in previous studies. Using the newly released Intel Xeon Phi many-core processor (with in-order cores) as an example, we show how we can use a set of microbenchmarks to determine various microarchitectural features of its memory system (many are undocumented from vendors).
    [Show full text]