8 research outputs found

    The "MIND" Scalable PIM Architecture

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    MIND (Memory, Intelligence, and Network Device) is an advanced parallel computer architecture for high performance computing and scalable embedded processing. It is a Processor-in-Memory (PIM) architecture integrating both DRAM bit cells and CMOS logic devices on the same silicon die. MIND is multicore with multiple memory/processor nodes on each chip and supports global shared memory across systems of MIND components. MIND is distinguished from other PIM architectures in that it incorporates mechanisms for efficient support of a global parallel execution model based on the semantics of message-driven multithreaded split-transaction processing. MIND is designed to operate either in conjunction with other conventional microprocessors or in standalone arrays of like devices. It also incorporates mechanisms for fault tolerance, real time execution, and active power management. This paper describes the major elements and operational methods of the MIND architecture

    Exploring the potential for accelerating sparse matrix-vector product on a Processing-in-Memory architecture

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    As the importance of memory access delays on performance has mushroomed over the past few decades, researchers have begun exploring Processing-in-Memory (PIM) technology, which offers higher memory bandwidth, lower memory latency, and lower power consumption. In this study, we investigate whether an emerging PIM design from Sandia National Laboratories can boost performance for sparse matrix-vector product (SMVP). While SMVP is in the best-case bandwidth-bound, factors related to matrix structure and representation also limit performance. We analyze SMVP both in the context of an AMD Opteron processor and the Sandia PIM, exploring the performance limiters for each and the degree to which these can be ameliorated by data and code transformations. Over a range of sparse matrices, SMVP on the PIM outperformed the Opteron by a factor of 1.82. On the PIM, computational kernel and data structure transformations improved performance by almost 40% over conventional implementations using compressed-sparse row format

    A Framework for Adaptable Operating and Runtime Systems

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    Doctor of Philosophy

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    dissertationIn-memory big data applications are growing in popularity, including in-memory versions of the MapReduce framework. The move away from disk-based datasets shifts the performance bottleneck from slow disk accesses to memory bandwidth. MapReduce is a data-parallel application, and is therefore amenable to being executed on as many parallel processors as possible, with each processor requiring high amounts of memory bandwidth. We propose using Near Data Computing (NDC) as a means to develop systems that are optimized for in-memory MapReduce workloads, offering high compute parallelism and even higher memory bandwidth. This dissertation explores three different implementations and styles of NDC to improve MapReduce execution. First, we use 3D-stacked memory+logic devices to process the Map phase on compute elements in close proximity to database splits. Second, we attempt to replicate the performance characteristics of the 3D-stacked NDC using only commodity memory and inexpensive processors to improve performance of both Map and Reduce phases. Finally, we incorporate fixed-function hardware accelerators to improve sorting performance within the Map phase. This dissertation shows that it is possible to improve in-memory MapReduce performance by potentially two orders of magnitude by designing system and memory architectures that are specifically tailored to that end

    Перспективні інформаційні технології та розвиток GRID-систем у високопродуктивних глобально-розподілених обчислювальних інфраструктурах корпоративної співпраці

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    Розглянуто GRID-технології як важливий та перспективний для України напрямок в організації ІТ-інфраструктур для корпоративного співробітництва і спільного використання розподілених обчислювальних й інформаційних ресурсів в урядуванні, бізнесі, науці й освіті. Виконано порівняння та аналіз можливостей розподілених технологій, типів GRID-систем, як засобу спільного використання ресурсів; напрямків подальших досліджень, використання, розвитку та впровадження GRID-технологій. Підкреслено необхідність координації робіт вітчизняних учених і фахівців у даному напрямку, який розвивається дуже динамічно.Рассмотрены GRID-технологии как важное и перспективное для Украины направление в организации ІТ-инфраструктур для корпоративного сотрудничества и совместного использования распределенных вычислительных и информационных ресурсов в государственном управлении, бизнесе, науке и образовании. Выполнены сравнения и анализ возможностей распределенных технологий, типов GRID-систем как средства совместного использования ресурсов, направлений дальнейших исследований, использования, развития и внедрения GRID-технологий. Подчеркнута необходимость координации работ отечественных ученых и специалистов в данном динамически развивающемся направлении.GRID-technologies as an important and promising direction for Ukraine in the organization of IT infrastructures for corporate cooperation and distributed computing and information resources sharing in the government, business, science and education are considered. Comparisons and the distributed technologies opportunities, GRID-system types, as resources sharing tool, directions of the further GRID technologies researches, usage, development and deployment are analyzed. Necessity of domestic scientists and experts activities coordination for the given dynamically developing discipline is strictly emphasized

    Exploring New Computing Paradigms for Data-Intensive Applications

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    L'abstract è presente nell'allegato / the abstract is in the attachmen

    새로운 메모리 기술을 기반으로 한 메모리 시스템 설계 기술

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    학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2017. 2. 최기영.Performance and energy efficiency of modern computer systems are largely dominated by the memory system. This memory bottleneck has been exacerbated in the past few years with (1) architectural innovations for improving the efficiency of computation units (e.g., chip multiprocessors), which shift the major cause of inefficiency from processors to memory, and (2) the emergence of data-intensive applications, which demands a large capacity of main memory and an excessive amount of memory bandwidth to efficiently handle such workloads. In order to address this memory wall challenge, this dissertation aims at exploring the potential of emerging memory technologies and designing a high-performance, energy-efficient memory hierarchy that is aware of and leverages the characteristics of such new memory technologies. The first part of this dissertation focuses on energy-efficient on-chip cache design based on a new non-volatile memory technology called Spin-Transfer Torque RAM (STT-RAM). When STT-RAM is used to build on-chip caches, it provides several advantages over conventional charge-based memory (e.g., SRAM or eDRAM), such as non-volatility, lower static power, and higher density. However, simply replacing SRAM caches with STT-RAM rather increases the energy consumption because write operations of STT-RAM are slower and more energy-consuming than those of SRAM. To address this challenge, we propose four novel architectural techniques that can alleviate the impact of inefficient STT-RAM write operations on system performance and energy consumption. First, we apply STT-RAM to instruction caches (where write operations are relatively infrequent) and devise a power-gating mechanism called LASIC, which leverages the non-volatility of STT-RAM to turn off STT-RAM instruction caches inside small loops. Second, we propose lower-bits cache, which exploits the narrow bit-width characteristics of application data by caching frequent bit-flips at lower bits in a small SRAM cache. Third, we present prediction hybrid cache, an SRAM/STT-RAM hybrid cache whose block placement between SRAM and STT-RAM is determined by predicting the write intensity of each cache block with a new hardware structure called write intensity predictor. Fourth, we propose DASCA, which predicts write operations that can bypass the cache without incurring extra cache misses (called dead writes) and lets the last-level cache bypass such dead writes to reduce write energy consumption. The second part of this dissertation architects intelligent main memory and its host architecture support based on logic-enabled DRAM. Traditionally, main memory has served the sole purpose of storing data because the extra manufacturing cost of implementing rich functionality (e.g., computation) on a DRAM die was unacceptably high. However, the advent of 3D die stacking now provides a practical, cost-effective way to integrate complex logic circuits into main memory, thereby opening up the possibilities for intelligent main memory. For example, it can be utilized to implement advanced memory management features (e.g., scheduling, power management, etc.) inside memoryit can be also used to offload computation to main memory, which allows us to overcome the memory bandwidth bottleneck caused by narrow off-chip channels (commonly known as processing-in-memory or PIM). The remaining questions are what to implement inside main memory and how to integrate and expose such new features to existing systems. In order to answer these questions, we propose four system designs that utilize logic-enabled DRAM to improve system performance and energy efficiency. First, we utilize the existing logic layer of a Hybrid Memory Cube (a commercial logic-enabled DRAM product) to (1) dynamically turn off some of its off-chip links by monitoring the actual bandwidth demand and (2) integrate prefetch buffer into main memory to perform aggressive prefetching without consuming off-chip link bandwidth. Second, we propose a scalable accelerator for large-scale graph processing called Tesseract, in which graph processing computation is offloaded to specialized processors inside main memory in order to achieve memory-capacity-proportional performance. Third, we design a low-overhead PIM architecture for near-term adoption called PIM-enabled instructions, where PIM operations are interfaced as cache-coherent, virtually-addressed host processor instructions that can be executed either by the host processor or in main memory depending on the data locality. Fourth, we propose an energy-efficient PIM system called aggregation-in-memory, which can adaptively execute PIM operations at any level of the memory hierarchy and provides a fully automated compiler toolchain that transforms existing applications to use PIM operations without programmer intervention.Chapter 1 Introduction 1 1.1 Inefficiencies in the Current Memory Systems 2 1.1.1 On-Chip Caches 2 1.1.2 Main Memory 2 1.2 New Memory Technologies: Opportunities and Challenges 3 1.2.1 Energy-Efficient On-Chip Caches based on STT-RAM 3 1.2.2 Intelligent Main Memory based on Logic-Enabled DRAM 6 1.3 Dissertation Overview 9 Chapter 2 Previous Work 11 2.1 Energy-Efficient On-Chip Caches based on STT-RAM 11 2.1.1 Hybrid Caches 11 2.1.2 Volatile STT-RAM 13 2.1.3 Redundant Write Elimination 14 2.2 Intelligent Main Memory based on Logic-Enabled DRAM 15 2.2.1 PIM Architectures in the 1990s 15 2.2.2 Modern PIM Architectures based on 3D Stacking 15 2.2.3 Modern PIM Architectures on Memory Dies 17 Chapter 3 Loop-Aware Sleepy Instruction Cache 19 3.1 Architecture 20 3.1.1 Loop Cache 21 3.1.2 Loop-Aware Sleep Controller 22 3.2 Evaluation and Discussion 24 3.2.1 Simulation Environment 24 3.2.2 Energy 25 3.2.3 Performance 27 3.2.4 Sensitivity Analysis 27 3.3 Summary 28 Chapter 4 Lower-Bits Cache 29 4.1 Architecture 29 4.2 Experiments 32 4.2.1 Simulator and Cache Model 32 4.2.2 Results 33 4.3 Summary 34 Chapter 5 Prediction Hybrid Cache 35 5.1 Problem and Motivation 37 5.1.1 Problem Definition 37 5.1.2 Motivation 37 5.2 Write Intensity Predictor 38 5.2.1 Keeping Track of Trigger Instructions 39 5.2.2 Identifying Hot Trigger Instructions 40 5.2.3 Dynamic Set Sampling 41 5.2.4 Summary 42 5.3 Prediction Hybrid Cache 43 5.3.1 Need for Write Intensity Prediction 43 5.3.2 Organization 43 5.3.3 Operations 44 5.3.4 Dynamic Threshold Adjustment 45 5.4 Evaluation Methodology 48 5.4.1 Simulator Configuration 48 5.4.2 Workloads 50 5.5 Single-Core Evaluations 51 5.5.1 Energy Consumption and Speedup 51 5.5.2 Energy Breakdown 53 5.5.3 Coverage and Accuracy 54 5.5.4 Sensitivity to Write Intensity Threshold 55 5.5.5 Impact of Dynamic Set Sampling 55 5.5.6 Results for Non-Write-Intensive Workloads 56 5.6 Multicore Evaluations 57 5.7 Summary 59 Chapter 6 Dead Write Prediction Assisted STT-RAM Cache 61 6.1 Motivation 62 6.1.1 Energy Impact of Inefficient Write Operations 62 6.1.2 Limitations of Existing Approaches 63 6.1.3 Potential of Dead Writes 64 6.2 Dead Write Classification 65 6.2.1 Dead-on-Arrival Fills 65 6.2.2 Dead-Value Fills 66 6.2.3 Closing Writes 66 6.2.4 Decomposition 67 6.3 Dead Write Prediction Assisted STT-RAM Cache Architecture 68 6.3.1 Dead Write Prediction 68 6.3.2 Bidirectional Bypass 71 6.4 Evaluation Methodology 72 6.4.1 Simulation Configuration 72 6.4.2 Workloads 74 6.5 Evaluation for Single-Core Systems 75 6.5.1 Energy Consumption and Speedup 75 6.5.2 Coverage and Accuracy 78 6.5.3 Sensitivity to Signature 78 6.5.4 Sensitivity to Update Policy 80 6.5.5 Implications of Device-/Circuit-Level Techniques for Write Energy Reduction 80 6.5.6 Impact of Prefetching 80 6.6 Evaluation for Multi-Core Systems 81 6.6.1 Energy Consumption and Speedup 81 6.6.2 Application to Inclusive Caches 83 6.6.3 Application to Three-Level Cache Hierarchy 84 6.7 Summary 85 Chapter 7 Link Power Management for Hybrid Memory Cubes 87 7.1 Background and Motivation 88 7.1.1 Hybrid Memory Cube 88 7.1.2 Motivation 89 7.2 HMC Link Power Management 91 7.2.1 Link Delay Monitor 91 7.2.2 Power State Transition 94 7.2.3 Overhead 95 7.3 Two-Level Prefetching 95 7.4 Application to Multi-HMC Systems 97 7.5 Experiments 98 7.5.1 Methodology 98 7.5.2 Link Energy Consumption and Speedup 100 7.5.3 HMC Energy Consumption 102 7.5.4 Runtime Behavior of LPM 102 7.5.5 Sensitivity to Slowdown Threshold 104 7.5.6 LPM without Prefetching 104 7.5.7 Impact of Prefetching on Link Traffic 105 7.5.8 On-Chip Prefetcher Aggressiveness in 2LP 107 7.5.9 Tighter Off-Chip Bandwidth Margin 107 7.5.10 Multithreaded Workloads 108 7.5.11 Multi-HMC Systems 109 7.6 Summary 111 Chapter 8 Tesseract PIM System for Parallel Graph Processing 113 8.1 Background and Motivation 115 8.1.1 Large-Scale Graph Processing 115 8.1.2 Graph Processing on Conventional Systems 117 8.1.3 Processing-in-Memory 118 8.2 Tesseract Architecture 119 8.2.1 Overview 119 8.2.2 Remote Function Call via Message Passing 122 8.2.3 Prefetching 124 8.2.4 Programming Interface 126 8.2.5 Application Mapping 127 8.3 Evaluation Methodology 128 8.3.1 Simulation Configuration 128 8.3.2 Workloads 129 8.4 Evaluation Results 130 8.4.1 Performance 130 8.4.2 Iso-Bandwidth Comparison 133 8.4.3 Execution Time Breakdown 134 8.4.4 Prefetch Efficiency 134 8.4.5 Scalability 135 8.4.6 Effect of Higher Off-Chip Network Bandwidth 136 8.4.7 Effect of Better Graph Distribution 137 8.4.8 Energy/Power Consumption and Thermal Analysis 138 8.5 Summary 139 Chapter 9 PIM-Enabled Instructions 141 9.1 Potential of ISA Extensions as the PIM Interface 143 9.2 PIM Abstraction 145 9.2.1 Operations 145 9.2.2 Memory Model 147 9.2.3 Software Modification 148 9.3 Architecture 148 9.3.1 Overview 148 9.3.2 PEI Computation Unit (PCU) 149 9.3.3 PEI Management Unit (PMU) 150 9.3.4 Virtual Memory Support 153 9.3.5 PEI Execution 153 9.3.6 Comparison with Active Memory Operations 154 9.4 Target Applications for Case Study 155 9.4.1 Large-Scale Graph Processing 155 9.4.2 In-Memory Data Analytics 156 9.4.3 Machine Learning and Data Mining 157 9.4.4 Operation Summary 157 9.5 Evaluation Methodology 158 9.5.1 Simulation Configuration 158 9.5.2 Workloads 159 9.6 Evaluation Results 159 9.6.1 Performance 160 9.6.2 Sensitivity to Input Size 163 9.6.3 Multiprogrammed Workloads 164 9.6.4 Balanced Dispatch: Idea and Evaluation 165 9.6.5 Design Space Exploration for PCUs 165 9.6.6 Performance Overhead of the PMU 167 9.6.7 Energy, Area, and Thermal Issues 167 9.7 Summary 168 Chapter 10 Aggregation-in-Memory 171 10.1 Motivation 173 10.1.1 Rethinking PIM for Energy Efficiency 173 10.1.2 Aggregation as PIM Operations 174 10.2 Architecture 176 10.2.1 Overview 176 10.2.2 Programming Model 177 10.2.3 On-Chip Caches 177 10.2.4 Coherence and Consistency 181 10.2.5 Main Memory 181 10.2.6 Potential Generalization Opportunities 183 10.3 Compiler Support 184 10.4 Contributions over Prior Art 185 10.4.1 PIM-Enabled Instructions 185 10.4.2 Parallel Reduction in Caches 187 10.4.3 Row Buffer Locality of DRAM Writes 188 10.5 Target Applications 188 10.6 Evaluation Methodology 190 10.6.1 Simulation Configuration 190 10.6.2 Hardware Overhead 191 10.6.3 Workloads 192 10.7 Evaluation Results 192 10.7.1 Energy Consumption and Performance 192 10.7.2 Dynamic Energy Breakdown 196 10.7.3 Comparison with Aggressive Writeback 197 10.7.4 Multiprogrammed Workloads 198 10.7.5 Comparison with Intrinsic-based Code 198 10.8 Summary 199 Chapter 11 Conclusion 201 11.1 Energy-Efficient On-Chip Caches based on STT-RAM 202 11.2 Intelligent Main Memory based on Logic-Enabled DRAM 203 Bibliography 205 요약 227Docto

    The Gilgamesh MIND Processor-in-Memory Architecture for Petaflops-Scale Computing

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