Memory Bandwidth and Latency in HPC: System Requirements and Performance Impact
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ASIC Implementation of DDR SDRAM Memory Controller
© 2016 IJEDR | Volume 4, Issue 4 | ISSN: 2321-9939 ASIC Implementation of DDR SDRAM Memory Controller Ramagiri Ramya, Naganaik M.Tech Scholar, ASST.PROF, HOD of ECE BRIG-IC, Hyderabad ________________________________________________________________________________________________________ Abstract - A Dedicated Memory Controller is of prime importance in applications that do not contain microprocessors (high-end applications). The Memory Controller provides command signals for memory refresh, read and write operation and initialization of SDRAM. Our work will focus on ASIC Design methodology of Double Data Rate (DDR) SDRAM Controller that is located between the DDR SDRAM and Bus Master. The Controller simplifies the SDRAM command interface to standard system read/write interface and also optimizes the access time of read/write cycle. Keywords - DDR SDRAM Controller, Read/Write Data path, Cadence RTL Compiler. ________________________________________________________________________________________________________ I. INTRODUCTION Memory devices are almost found in all systems and nowadays high speed and high performance memories are in great demand. For better throughput and speed, the controllers are to be designed with clock frequency in the range of megahertz. As the clock speed of the controller is increasing, the design challenges are also becoming complex. Therefore the next generation memory devices require very high speed controllers like double data rate and quad data rate memory controllers. In this paper, the double data rate SDRAM Controller is implemented using ASIC methodology. Synchronous DRAM (SDRAM) is preferred in embedded system memory design because of its speed and pipelining capability. In high-end applications, like microprocessors there will be specific built in peripherals to provide the interface to the SDRAM. But for other applications, the system designer must design a specific memory controller to provide command signals for memory refresh, read and write operation and initialization of SDRAM. -
Investigations on Hardware Compression of IBM Power9 Processors
Investigations on hardware compression of IBM Power9 processors Jérome Kieffer, Pierre Paleo, Antoine Roux, Benoît Rousselle Outline ● The bandwidth issue at synchrotrons sources ● Presentation of the evaluated systems: – Intel Xeon vs IBM Power9 – Benchmarks on bandwidth ● The need for compression of scientific data – Compression as part of HDF5 – The hardware compression engine NX-gzip within Power9 – Gzip performance benchmark – Bitshuffle-LZ4 benchmark – Filter optimizations – Benchmark of parallel filtered gzip ● Conclusions – on the hardware – on the compression pipeline in HDF5 Page 2 HDF5 on Power9 18/09/2019 Bandwidth issue at synchrotrons sources Data analysis computer with the main interconnections and their associated bandwidth. Data reduction Upgrade to → Azimuthal integration 100 Gbit/s Data compression ! Figures from former generation of servers Kieffer et al. Volume 25 | Part 2 | March 2018 | Pages 612–617 | 10.1107/S1600577518000607 Page 3 HDF5 on Power9 18/09/2019 Topologies of Intel Xeon servers in 2019 Source: intel.com Page 4 HDF5 on Power9 18/09/2019 Architecture of the AC922 server from IBM featuring Power9 Credit: Thibaud Besson, IBM France Page 6 HDF5 on Power9 18/09/2019 Bandwidth measurement: Xeon vs Power9 Computer Dell R840 IBM AC922 Processor 4 Intel Xeon (12 cores) 2 IBM Power9 (16 cores) 2.6 GHz 2.7 GHz Cache (L3) 19 MB 8x 10 MB Memory channels 4x 6 DDR4 2x 8 DDR4 Memory capacity → 3TB → 2TB Memory speed theory 512 GB/s 340 GB/s Measured memory speed 160 GB/s 270 GB/s Interconnects PCIe v3 PCIe v4 NVlink2 & CAPI2 GP-GPU co-processor 2Tesla V100 PCIe v3 2Tesla V100 NVlink2 Interconnect speed 12 GB/s 48 GB/s CPU ↔ GPU Page 8 HDF5 on Power9 18/09/2019 Strength and weaknesses of the OpenPower architecture While amd64 is today’s de facto standard in HPC, it has a few competitors: arm64, ppc64le and to a less extend riscv and mips64. -
Performance Impact of Memory Channels on Sparse and Irregular Algorithms
Performance Impact of Memory Channels on Sparse and Irregular Algorithms Oded Green1,2, James Fox2, Jeffrey Young2, Jun Shirako2, and David Bader3 1NVIDIA Corporation — 2Georgia Institute of Technology — 3New Jersey Institute of Technology Abstract— Graph processing is typically considered to be a Graph algorithms are typically latency-bound if there is memory-bound rather than compute-bound problem. One com- not enough parallelism to saturate the memory subsystem. mon line of thought is that more available memory bandwidth However, the growth in parallelism for shared-memory corresponds to better graph processing performance. However, in this work we demonstrate that the key factor in the utilization systems brings into question whether graph algorithms are of the memory system for graph algorithms is not necessarily still primarily latency-bound. Getting peak or near-peak the raw bandwidth or even the latency of memory requests. bandwidth of current memory subsystems requires highly Instead, we show that performance is proportional to the parallel applications as well as good spatial and temporal number of memory channels available to handle small data memory reuse. The lack of spatial locality in sparse and transfers with limited spatial locality. Using several widely used graph frameworks, including irregular algorithms means that prefetched cache lines have Gunrock (on the GPU) and GAPBS & Ligra (for CPUs), poor data reuse and that the effective bandwidth is fairly low. we evaluate key graph analytics kernels using two unique The introduction of high-bandwidth memories like HBM and memory hierarchies, DDR-based and HBM/MCDRAM. Our HMC have not yet closed this inefficiency gap for latency- results show that the differences in the peak bandwidths sensitive accesses [19]. -
IBM Power System POWER8 Facts and Features
IBM Power Systems IBM Power System POWER8 Facts and Features April 29, 2014 IBM Power Systems™ servers and IBM BladeCenter® blade servers using IBM POWER7® and POWER7+® processors are described in a separate Facts and Features report dated July 2013 (POB03022-USEN-28). IBM Power Systems™ servers and IBM BladeCenter® blade servers using IBM POWER6® and POWER6+™ processors are described in a separate Facts and Features report dated April 2010 (POB03004-USEN-14). 1 IBM Power Systems Table of Contents IBM Power System S812L 4 IBM Power System S822 and IBM Power System S822L 5 IBM Power System S814 and IBM Power System S824 6 System Unit Details 7 Server I/O Drawers & Attachment 8 Physical Planning Characteristics 9 Warranty / Installation 10 Power Systems Software Support 11 Performance Notes & More Information 12 These notes apply to the description tables for the pages which follow: Y Standard / Supported Optional Optionally Available / Supported N/A or - Not Available / Supported or Not Applicable SOD Statement of General Direction announced SLES SUSE Linux Enterprise Server RHEL Red Hat Enterprise Linux a One x8 PCIe slots must contain a 4-port 1Gb Ethernet LAN available for client use b Use of expanded function storage backplane uses one PCIe slot Backplane provides dual high performance SAS controllers with 1.8 GB write cache expanded up to 7.2 GB with c compression plus Easy Tier function plus two SAS ports for running an EXP24S drawer d Full benchmark results are located at ibm.com/systems/power/hardware/reports/system_perf.html e Option is supported on IBM i only through VIOS. -
March 11, 2010 Presentation
IBM Power Systems POWER7TM Announcement, The Next Generation of Power Systems Power your planet. February 25, 2010 IBM Power Systems 2 February 25, 2010 IBM Power Systems POWER7 System Highlights .Balance System Design - Cache, Memory, and IO .POWER7 Processor Technology - 6th Implementation of multi-core design - On chip L2 & L3 caches .POWER7 System Architecture - Blades to High End offerings - Enhances memory implementation - PCIe, SAS / SATA .Built in Virtualization - Memory Expansion - VM Control .Green Technologies - Processor Nap & Sleep Mode - Memory Power Down support - Aggressive Power Save / Capping Modes 600 500 .Availability 400 - Processor Instruction Retry 300 - Alternate Process Recovery 200 100 - Concurrent Add & Services 0 JS23 JS43 520 550 560 570/16 570/32 595 3 February 25, 2010 IBM Power Systems 4 February 25, 2010 IBM Power Systems Power Processor Technology IBM investment in the Power Franchise Dependable Execution for a decade POWER8 POWER7 45 nm Globalization and globally POWER6 available resources 65 nm •Performance/System Capacity POWER5 •4-5X increase from Power6 130 nm •Multi Core – Up to 8 POWER4 •SMT4 – 4 threads/core 180 nm . Dual Core •On-Chip eDRAM . High Frequencies • Energy . Dual Core . Virtualization + . Enhanced Scaling • Efficiency: 3-4X Power6 . Memory Subsystem + . SMT • Dynamic Energy . Dual Core . Altivec . Distributed Switch + Management . Chip Multi Processing . Instruction Retry . Distributed Switch . Core Parallelism + • Reliability + . Dyn Energy Mgmt . Shared L2 . FP Performance + . SMT + •Memory DIMM – DRAM . Dynamic LPARs (32) . Memory bandwidth + . Protection Keys Sparing . Virtualization •N+2 Voltage Regulator Redundancy •Protection Keys + 5 February 25, 2010 IBM Power Systems POWER6 – POWER7 Compare Wireless world Mobile platforms are developing as new means of identification. -
POWER® Processor-Based Systems
IBM® Power® Systems RAS Introduction to IBM® Power® Reliability, Availability, and Serviceability for POWER9® processor-based systems using IBM PowerVM™ With Updates covering the latest 4+ Socket Power10 processor-based systems IBM Systems Group Daniel Henderson, Irving Baysah Trademarks, Copyrights, Notices and Acknowledgements Trademarks IBM, the IBM logo, and ibm.com are trademarks or registered trademarks of International Business Machines Corporation in the United States, other countries, or both. These and other IBM trademarked terms are marked on their first occurrence in this information with the appropriate symbol (® or ™), indicating US registered or common law trademarks owned by IBM at the time this information was published. Such trademarks may also be registered or common law trademarks in other countries. A current list of IBM trademarks is available on the Web at http://www.ibm.com/legal/copytrade.shtml The following terms are trademarks of the International Business Machines Corporation in the United States, other countries, or both: Active AIX® POWER® POWER Power Power Systems Memory™ Hypervisor™ Systems™ Software™ Power® POWER POWER7 POWER8™ POWER® PowerLinux™ 7® +™ POWER® PowerHA® POWER6 ® PowerVM System System PowerVC™ POWER Power Architecture™ ® x® z® Hypervisor™ Additional Trademarks may be identified in the body of this document. Other company, product, or service names may be trademarks or service marks of others. Notices The last page of this document contains copyright information, important notices, and other information. Acknowledgements While this whitepaper has two principal authors/editors it is the culmination of the work of a number of different subject matter experts within IBM who contributed ideas, detailed technical information, and the occasional photograph and section of description. -
Openpower AI CERN V1.Pdf
Moore’s Law Processor Technology Firmware / OS Linux Accelerator sSoftware OpenStack Storage Network ... Price/Performance POWER8 2000 2020 DRAM Memory Chips Buffer Power8: Up to 12 Cores, up to 96 Threads L1, L2, L3 + L4 Caches Up to 1 TB per socket https://www.ibm.com/blogs/syst Up to 230 GB/s sustained memory ems/power-systems- openpower-enable- bandwidth acceleration/ System System Memory Memory 115 GB/s 115 GB/s POWER8 POWER8 CPU CPU NVLink NVLink 80 GB/s 80 GB/s P100 P100 P100 P100 GPU GPU GPU GPU GPU GPU GPU GPU Memory Memory Memory Memory GPU PCIe CPU 16 GB/s System bottleneck Graphics System Memory Memory IBM aDVantage: data communication and GPU performance POWER8 + 78 ms Tesla P100+NVLink x86 baseD 170 ms GPU system ImageNet / Alexnet: Minibatch size = 128 ADD: Coherent Accelerator Processor Interface (CAPI) FPGA CAPP PCIe POWER8 Processor ...FPGAs, networking, memory... Typical I/O MoDel Flow Copy or Pin MMIO Notify Poll / Int Copy or Unpin Ret. From DD DD Call Acceleration Source Data Accelerator Completion Result Data Completion Flow with a Coherent MoDel ShareD Mem. ShareD Memory Acceleration Notify Accelerator Completion Focus on Enterprise Scale-Up Focus on Scale-Out and Enterprise Future Technology and Performance DriVen Cost and Acceleration DriVen Partner Chip POWER6 Architecture POWER7 Architecture POWER8 Architecture POWER9 Architecture POWER10 POWER8/9 2007 2008 2010 2012 2014 2016 2017 TBD 2018 - 20 2020+ POWER6 POWER6+ POWER7 POWER7+ POWER8 POWER8 P9 SO P9 SU P9 SO 2 cores 2 cores 8 cores 8 cores 12 cores w/ NVLink -
IBM Power System E850 the Most Agile 4-Socket System in the Marketplace, Optimized for Performance, Reliability and Expansion
IBM Systems Data Sheet IBM Power System E850 The most agile 4-socket system in the marketplace, optimized for performance, reliability and expansion Businesses today are demanding faster insights that analyze more data in Highlights new ways. They need to implement applications in days versus months, and they need to achieve all these goals while reducing IT costs. This is ●● ●●Designed for data and analytics, delivers creating new demands on IT infrastructures, requiring new levels of per- secure, reliable performance in a compact, 4-socket system formance and the flexibility to respond to new business opportunities, all at an affordable price. ●● ●●Can flexibly scale to rapidly respond to changing business needs The IBM® Power® System E850 server offers a unique blend of ●● ●●Can reduce IT costs through application enterprise-class capabilities in a space-efficient, 4-socket system with consolidation, higher availability and excellent price performance. With up to 48 IBM POWER8™ processor virtualization to yield over 70 percent utilization cores, advanced IBM PowerVM® virtualization that can yield over 70 percent system utilization and Capacity on Demand (CoD), no other 4-socket system in the industry delivers this combination of performance, efficiency and business agility. These capabilities make the Power E850 server an ideal platform for medium-size businesses and as a departmental server or data center building block for large enterprises. Designed for the demands of big data and analytics Businesses are amassing a wealth of data and IBM Power Systems™, built with innovation to support today’s data demands, can store it, secure it and, most important, extract actionable insight from it. -
A Modern Primer on Processing in Memory
A Modern Primer on Processing in Memory Onur Mutlua,b, Saugata Ghoseb,c, Juan Gomez-Luna´ a, Rachata Ausavarungnirund SAFARI Research Group aETH Z¨urich bCarnegie Mellon University cUniversity of Illinois at Urbana-Champaign dKing Mongkut’s University of Technology North Bangkok Abstract Modern computing systems are overwhelmingly designed to move data to computation. This design choice goes directly against at least three key trends in computing that cause performance, scalability and energy bottlenecks: (1) data access is a key bottleneck as many important applications are increasingly data-intensive, and memory bandwidth and energy do not scale well, (2) energy consumption is a key limiter in almost all computing platforms, especially server and mobile systems, (3) data movement, especially off-chip to on-chip, is very expensive in terms of bandwidth, energy and latency, much more so than computation. These trends are especially severely-felt in the data-intensive server and energy-constrained mobile systems of today. At the same time, conventional memory technology is facing many technology scaling challenges in terms of reliability, energy, and performance. As a result, memory system architects are open to organizing memory in different ways and making it more intelligent, at the expense of higher cost. The emergence of 3D-stacked memory plus logic, the adoption of error correcting codes inside the latest DRAM chips, proliferation of different main memory standards and chips, specialized for different purposes (e.g., graphics, low-power, high bandwidth, low latency), and the necessity of designing new solutions to serious reliability and security issues, such as the RowHammer phenomenon, are an evidence of this trend. -
IBM Power Systems Performance Report Apr 13, 2021
IBM Power Performance Report Power7 to Power10 September 8, 2021 Table of Contents 3 Introduction to Performance of IBM UNIX, IBM i, and Linux Operating System Servers 4 Section 1 – SPEC® CPU Benchmark Performance 4 Section 1a – Linux Multi-user SPEC® CPU2017 Performance (Power10) 4 Section 1b – Linux Multi-user SPEC® CPU2017 Performance (Power9) 4 Section 1c – AIX Multi-user SPEC® CPU2006 Performance (Power7, Power7+, Power8) 5 Section 1d – Linux Multi-user SPEC® CPU2006 Performance (Power7, Power7+, Power8) 6 Section 2 – AIX Multi-user Performance (rPerf) 6 Section 2a – AIX Multi-user Performance (Power8, Power9 and Power10) 9 Section 2b – AIX Multi-user Performance (Power9) in Non-default Processor Power Mode Setting 9 Section 2c – AIX Multi-user Performance (Power7 and Power7+) 13 Section 2d – AIX Capacity Upgrade on Demand Relative Performance Guidelines (Power8) 15 Section 2e – AIX Capacity Upgrade on Demand Relative Performance Guidelines (Power7 and Power7+) 20 Section 3 – CPW Benchmark Performance 19 Section 3a – CPW Benchmark Performance (Power8, Power9 and Power10) 22 Section 3b – CPW Benchmark Performance (Power7 and Power7+) 25 Section 4 – SPECjbb®2015 Benchmark Performance 25 Section 4a – SPECjbb®2015 Benchmark Performance (Power9) 25 Section 4b – SPECjbb®2015 Benchmark Performance (Power8) 25 Section 5 – AIX SAP® Standard Application Benchmark Performance 25 Section 5a – SAP® Sales and Distribution (SD) 2-Tier – AIX (Power7 to Power8) 26 Section 5b – SAP® Sales and Distribution (SD) 2-Tier – Linux on Power (Power7 to Power7+) -
Case Study on Integrated Architecture for In-Memory and In-Storage Computing
electronics Article Case Study on Integrated Architecture for In-Memory and In-Storage Computing Manho Kim 1, Sung-Ho Kim 1, Hyuk-Jae Lee 1 and Chae-Eun Rhee 2,* 1 Department of Electrical Engineering, Seoul National University, Seoul 08826, Korea; [email protected] (M.K.); [email protected] (S.-H.K.); [email protected] (H.-J.L.) 2 Department of Information and Communication Engineering, Inha University, Incheon 22212, Korea * Correspondence: [email protected]; Tel.: +82-32-860-7429 Abstract: Since the advent of computers, computing performance has been steadily increasing. Moreover, recent technologies are mostly based on massive data, and the development of artificial intelligence is accelerating it. Accordingly, various studies are being conducted to increase the performance and computing and data access, together reducing energy consumption. In-memory computing (IMC) and in-storage computing (ISC) are currently the most actively studied architectures to deal with the challenges of recent technologies. Since IMC performs operations in memory, there is a chance to overcome the memory bandwidth limit. ISC can reduce energy by using a low power processor inside storage without an expensive IO interface. To integrate the host CPU, IMC and ISC harmoniously, appropriate workload allocation that reflects the characteristics of the target application is required. In this paper, the energy and processing speed are evaluated according to the workload allocation and system conditions. The proof-of-concept prototyping system is implemented for the integrated architecture. The simulation results show that IMC improves the performance by 4.4 times and reduces total energy by 4.6 times over the baseline host CPU. -
Towards a Portable Hierarchical View of Distributed Shared Memory Systems: Challenges and Solutions
Towards A Portable Hierarchical View of Distributed Shared Memory Systems: Challenges and Solutions Millad Ghane Sunita Chandrasekaran Margaret S. Cheung Department of Computer Science Department of Computer and Information Physics Department University of Houston Sciences University of Houston TX, USA University of Delaware Center for Theoretical Biological Physics, [email protected],[email protected] DE, USA Rice University [email protected] TX, USA [email protected] Abstract 1 Introduction An ever-growing diversity in the architecture of modern super- Heterogeneity has become increasingly prevalent in the recent computers has led to challenges in developing scientifc software. years given its promising role in tackling energy and power con- Utilizing heterogeneous and disruptive architectures (e.g., of-chip sumption crisis of high-performance computing (HPC) systems [15, and, in the near future, on-chip accelerators) has increased the soft- 20]. Dennard scaling [14] has instigated the adaptation of heteroge- ware complexity and worsened its maintainability. To that end, we neous architectures in the design of supercomputers and clusters need a productive software ecosystem that improves the usability by the HPC community. The July 2019 TOP500 [36] report shows and portability of applications for such systems while allowing how 126 systems in the list are heterogeneous systems confgured every parallelism opportunity to be exploited. with one or many GPUs. This is the prevailing trend in current gen- In this paper, we outline several challenges that we encountered eration of supercomputers. As an example, Summit [31], the fastest in the implementation of Gecko, a hierarchical model for distributed supercomputer according to the Top500 list (June 2019) [36], has shared memory architectures, using a directive-based program- two IBM POWER9 processors and six NVIDIA Volta V100 GPUs.