Compiler-Directed Energy Savings in Superscalar Processors

Institute for Computing Systems ArchitectureSuperscalar processors contain large, complex structures to hold data and instructions as they wait to be executed. However, many of these structures consume large amounts of energy, making them hotspots requiring sophisticated cooling systems. With the trend towards larger, more complex processors, this will become more of a problem, having important implications for future technology. This thesis uses compiler-based optimisation schemes to target the issue queue and register file. These are two of the most energy consuming structures in the processor. The algorithms and hardware techniques developed in this work dynamically adapt the processor's resources to the changing program phases, turning off parts of each structure when they are unused to save dynamic and static energy. To optimise the issue queue, the compiler analysis tracks data dependences through each program procedure. It identifies the critical path through each program region and informs the hardware of the minimum number of queue entries required to prevent it slowing down. This reduces the occupancy of the queue and increases the opportunities to save energy. With just a 1.3% performance loss, 26% dynamic and 32% static energy savings are achieved. Registers can be idle for many cycles after they are last read, before they are released and put back on the free-list to be reused by another instruction. Alternatively, they can be turned off for energy savings. Early register releasing can be used to perform this operation sooner than usual, but hardware schemes must wait for the instruction redefining the relevant logical register to enter the pipeline. This thesis presents an exploration of compiler-directed early register releasing. The compiler can exactly identify the last use of each register and pass the information to the hardware, based on simple data-flow and liveness analysis. The best scheme achieves 15% dynamic and 19% static energy savings. Finally, the issue queue limiting and early register releasing schemes are combined for energy savings in both processor structures. Four different configurations are evaluated bringing 25% to 31% dynamic and 19% to 34% static issue queue energy savings and reductions of 18% to 25% dynamic and 20% to 21% static energy in the register file

Design and implementation of an out of order execution engine of floating point arithmetic operations

In this thesis, work is undertaken towards the design in hardware description languages and implementation in FPGA of an out of order execution engine of floating point arithmetic operations. This thesis work, is part of a project called Lagarto

Efficient Scaling of Out-of-Order Processor Resources

Rather than improving single-threaded performance, with the dawn of the multi-core era, processor microarchitects have exploited Moore's law transistor scaling by increasing core density on a chip and increasing the number of thread contexts within a core. However, single-thread performance and efficiency is still very relevant in the power-constrained multi-core era, as increasing core counts do not yield corresponding performance improvements under real thermal and thread-level constraints. This dissertation provides a detailed study of register reference count structures and its application to both conventional and non-conventional, latency tolerant, out-of-order processors. Prior work has incorporated reference counting, but without a detailed implementation or energy model. This dissertation presents a working implementation of reference count structures and shows the overheads are low and can be recouped by the techniques enabled in high-performance out-of-order processors. A study of register allocation algorithms exploits register file occupancy to reduce power consumption by dynamically resizing the register file, which is especially important in the face of wider multi-threaded processors who require larger register files. Latency tolerance has been introduced as a technique to improve single threaded performance by removing cache-miss dependent instructions from the execution pipeline until the miss returns. This dissertation introduces a microarchitecture with a predictive approach to identify long-latency loads, and reduce the energy cost and overhead of scaling the instruction window inherent in latency tolerant microarchitectures. The key features include a front-end predictive slice-out mechanism and in-order queue structure along with mechanisms to reduce the energy cost and register-file usage of executing instructions. Cycle-level simulation shows improved performance and reduced energy delay for memory-bound workloads. Both techniques scale processor resources, addressing register file inefficiency and the allocation of processor resources to instructions during low ILP regions.Ph.D., Computer Engineering -- Drexel University, 201

Improving the Energy Efficiency of Microprocessor Cores Through Accurate Resource Utilisation Prediction

CMOS technology scaling improves the speed and functionality of microprocessors by reducing the size of transistors. Static power dissipation also increases as a result of scaling however, and has been identified as a limiting factor in technology scaling. As current technology approaches that limit, techniques are required both at the technology-level and in the architecture design to reduce sub-threshold leakage, which accounts for the majority of static power dissipation. This thesis presents an approach to predict the idle periods of execution units at runtime and power-gate them during these periods to eliminate their static power leakage. We exploit similar execution characteristics across loop iterations to build a prediction of the units required to execute an entire loop from the units used over the first few iterations. The utilisation of each execution unit is monitored for each iteration, and thresholds are used to determine which units should be power-gated for the remainder of the loop. Three techniques are presented: Loop-Directed Mothballing (LDM), Extended Loop-Directed Mothballing (ELDM) and schedule balancing. LDM power-gates execution units only during innermost loops, which are simple to detect at runtime. ELDM extends this method to all loops using loop entry and exit information gathered offline. The balancing scheduler is developed to balance the types of instruction issued each cycle, to encourage reuse of execution units and make unnecessary units easier to detect. Extensive simulation using traces of 16 benchmarks from the SPEC CPU2006 suite demonstrates that LDM reduces the energy-delay product of our simulated superscalar processor by 10.3%. For traces with a low proportion of executed instructions inside innermost loops, ELDM improves the energy-delay product by up to 13% by allowing the technique to be applied to other loops in the trace. Employing schedule balancing with ELDM achieves similar savings, and simplifies the hardware required to make predictions

Doctor of Philosophy

dissertationWith the explosion of chip transistor counts, the semiconductor industry has struggled with ways to continue scaling computing performance in line with historical trends. In recent years, the de facto solution to utilize excess transistors has been to increase the size of the on-chip data cache, allowing fast access to an increased portion of main memory. These large caches allowed the continued scaling of single thread performance, which had not yet reached the limit of instruction level parallelism (ILP). As we approach the potential limits of parallelism within a single threaded application, new approaches such as chip multiprocessors (CMP) have become popular for scaling performance utilizing thread level parallelism (TLP). This dissertation identifies the operating system as a ubiquitous area where single threaded performance and multithreaded performance have often been ignored by computer architects. We propose that novel hardware and OS co-design has the potential to significantly improve current chip multiprocessor designs, enabling increased performance and improved power efficiency. We show that the operating system contributes a nontrivial overhead to even the most computationally intense workloads and that this OS contribution grows to a significant fraction of total instructions when executing several common applications found in the datacenter. We demonstrate that architectural improvements have had little to no effect on the performance of the OS over the last 15 years, leaving ample room for improvements. We specifically consider three potential solutions to improve OS execution on modern processors. First, we consider the potential of a separate operating system processor (OSP) operating concurrently with general purpose processors (GPP) in a chip multiprocessor organization, with several specialized structures acting as efficient conduits between these processors. Second, we consider the potential of segregating existing caching structures to decrease cache interference between the OS and application. Third, we propose that there are components within the OS itself that should be refactored to be both multithreaded and cache topology aware, which in turn, improves the performance and scalability of many-threaded applications

Microarchitectural techniques to reduce energy consumption in the memory hierarchy

This thesis states that dynamic profiling of the memory reference stream can improve energy and performance in the memory hierarchy. The research presented in this theses provides multiple instances of using lightweight hardware structures to profile the memory reference stream. The objective of this research is to develop microarchitectural techniques to reduce energy consumption at different levels of the memory hierarchy. Several simple and implementable techniques were developed as a part of this research. One of the techniques identifies and eliminates redundant refresh operations in DRAM and reduces DRAM refresh power. Another, reduces leakage energy in L2 and higher level caches for multiprocessor systems. The emphasis of this research has been to develop several techniques of obtaining energy savings in caches using a simple hardware structure called the counting Bloom filter (CBF). CBFs have been used to predict L2 cache misses and obtain energy savings by not accessing the L2 cache on a predicted miss. A simple extension of this technique allows CBFs to do way-estimation of set associative caches to reduce energy in cache lookups. Another technique using CBFs track addresses in a Virtual Cache and reduce false synonym lookups. Finally this thesis presents a technique to reduce dynamic power consumption in level one caches using significance compression. The significant energy and performance improvements demonstrated by the techniques presented in this thesis suggest that this work will be of great value for designing memory hierarchies of future computing platforms.Ph.D.Committee Chair: Lee, Hsien-Hsin S.; Committee Member: Cahtterjee,Abhijit; Committee Member: Mukhopadhyay, Saibal; Committee Member: Pande, Santosh; Committee Member: Yalamanchili, Sudhaka

Tiled microprocessors

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 251-258).Current-day microprocessors have reached the point of diminishing returns due to inherent scalability limitations. This thesis examines the tiled microprocessor, a class of microprocessor which is physically scalable but inherits many of the desirable properties of conventional microprocessors. Tiled microprocessors are composed of an array of replicated tiles connected by a special class of network, the Scalar Operand Network (SON), which is optimized for low-latency, low-occupancy communication between remote ALUs on different tiles. Tiled microprocessors can be constructed to scale to 100's or 1000's of functional units. This thesis identifies seven key criteria for achieving physical scalability in tiled microprocessors. It employs an archetypal tiled microprocessor to examine the challenges in achieving these criteria and to explore the properties of Scalar Operand Networks. The thesis develops the field of SONs in three major ways: it introduces the 5-tuple performance metric, it describes a complete, high-frequency SON implementation, and it proposes a taxonomy, called AsTrO, for categorizing them.(cont.) To develop these ideas, the thesis details the design, implementation and analysis of a tiled microprocessor prototype, the Raw Microprocessor, which was implemented at MIT in 180 nm technology. Overall, compared to Raw, recent commercial processors with half the transistors required 30x as many lines of code, occupied 100x as many designers, contained 50x as many pre-tapeout bugs, and resulted in 33x as many post-tapeout bugs. At the same time, the Raw microprocessor proves to be more versatile in exploiting ILP, stream, and server-farm workloads with modest to large amounts of parallelism.by Michael Bedford Taylor.Ph.D

Analysis and acceleration of data mining algorithms on high performance reconfigurable computing platforms

With the continued development of computation and communication technologies, we are overwhelmed with electronic data. Ubiquitous data in governments, commercial enterprises, universities and various organizations records our decisions, transactions and thoughts. The data collection rate is undergoing tremendous increase. And there is no end in sight. On one hand, as the volume of data explodes, the gap between the human being\u27s understanding of the data and the knowledge hidden in the data will be enlarged. The algorithms and techniques, collectively known as data mining, are emerged to bridge the gap. The data mining algorithms are usually data-compute intensive. On the other hand, the overall computing system performance is not increasing at an equal rate. Consequently, there is strong requirement to design special computing systems to accelerate data mining applications. FPGAs based High Performance Reconfigurable Computing(HPRC) system is to design optimized hardware architecture for a given problem. The increased gate count, arithmetic capability, and other features of modern FPGAs now allow researcher to implement highly complicated reconfigurable computational architecture. In contrast with ASICs, FPGAs have the advantages of low power, low nonrecurring engineering costs, high design flexibility and the ability to update functionality after shipping. In this thesis, we first design the architectures for data intensive and data-compute intensive applications respectively. Then we present a general HPRC framework for data mining applications: Frequent Pattern Mining(FPM) is a data-compute intensive application which is to find commonly occurring itemsets in databases. We use systolic tree architecture in FPGA hardware to mimic the internal memory layout of FP-growth algorithm while achieving higher throughput. The experimental results demonstrate that the proposed hardware architecture is faster than the software approach. Sparse Matrix-Vector Multiplication(SMVM) is a data-intensive application which is an important computing core in many applications. We present a scalable and efficient FPGA-based SMVM architecture which can handle arbitrary matrix sizes without preprocessing or zero padding and can be dynamically expanded based on the available I/O bandwidth. The experimental results using a commercial FPGA-based acceleration system demonstrate that our reconfigurable SMVM engine is more efficient than existing state-of-the-art, with speedups over a highly optimized software implementation of 2.5X to 6.5X, depending on the sparsity of the input benchmark. Accelerating Text Classification Using SMVM is performed in Convey HC-1 HPRC platform. The SMVM engines are deployed into multiple FPGA chips. Text documents are represented as large sparse matrices using Vector Space Model(VSM). The k-nearest neighbor algorithm uses SMVM to perform classification simultaneously on multiple FPGAs. Our experiment shows that the classification in Convey HC-1 is several times faster compared with the traditional computing architecture. MapReduce Reconfigurable Framework for Data Mining Applications is a pipelined and high performance framework for FPGA design based on the MapReduce model. Our goal is to lessen the FPGA programmer burden while minimizing performance degradation. The designer only need focus on the mapper and reducer modules design. We redesigned the SMVM architecture using the MapReduce Framework. The manual VHDL code is only 15 percent of that used in the customized architecture

Parallel and Distributed Computing

The 14 chapters presented in this book cover a wide variety of representative works ranging from hardware design to application development. Particularly, the topics that are addressed are programmable and reconfigurable devices and systems, dependability of GPUs (General Purpose Units), network topologies, cache coherence protocols, resource allocation, scheduling algorithms, peertopeer networks, largescale network simulation, and parallel routines and algorithms. In this way, the articles included in this book constitute an excellent reference for engineers and researchers who have particular interests in each of these topics in parallel and distributed computing

A novel access pattern-based multi-core memory architecture

Increasingly High-Performance Computing (HPC) applications run on heterogeneous multi-core platforms. The basic reason of the growing popularity of these architectures is their low power consumption, and high throughput oriented nature. However, this throughput imposes a requirement on the data to be supplied in a high throughput manner for the multi-core system. This results in the necessity of an efficient management of on-chip and off-chip memory data transfers, which is a significant challenge. Complex regular and irregular memory data transfer patterns are becoming widely dominant for a range of application domains including the scientific, image and signal processing. Data accesses can be arranged in independent patterns that an efficient memory management can exploit. The software based approaches using general purpose caches and on-chip memories are beneficial to some extent. However, the task of efficient data management for the throughput oriented devices could be improved by providing hardware mechanisms that exploit the knowledge of access patterns in memory management and scheduling of accesses for a heterogeneous multi-core architecture. The focus of this thesis is to present architectural explorations for a novel access pattern-based multi-core memory architecture. In general, the thesis covers four main aspects of memory system in this research. These aspects can be categorized as: i) Uni-core Memory System for Regular Data Pattern. ii) Multi-core Memory System for Regular Data Pattern. iii) Uni-core Memory System for Irregular Data Pattern. and iv) Multi-core Memory System for Irregular Data Pattern.Les aplicacions de computació d'alt rendiment (HPC) s'executen cada vegada més en plataformes heterogènies de múltiples nuclis. El motiu bàsic de la creixent popularitat d'aquestes arquitectures és el seu baix consum i la seva natura orientada a alt throughput. No obstant, aquest thoughput imposa el requeriment de que les dades es proporcionin al sistema també amb alt throughput. Això resulta en la necessitat de gestionar eficientment les trasferències de memòria (dins i fora del chip), un repte significatiu. Els patrons de transferències de memòria regulars però complexos així com els irregulars són cada vegada més dominants per a diversos dominis d'aplicacions, incloent el científic i el processat d'imagte i senyals. Aquests accessos a dades poden ser organitzats en patrons independents que un gestor de memòria eficient pot explotar. Els mètodes basats en programari emprant memòries cau de propòsit general i memòries al chip són beneficioses fins a cert punt. No obstant, la tasca de gestionar eficientment les transferències de dades per a dispositius orientats a throughput pot ser millorada oferint mecanismes hardware que explotin el coneixement dels patrons d'accés de les aplicacions, així com la planificació dels accessos a una arquitectura de múltiples nuclis. Aquesta tesis està enfocada a explorar una arquitectura de memòria novedosa per a processadors de múltiples nuclis, basada en els patrons d'accés. En general, la recerca de la tesis cobreix quatres aspectes principals del sistema de memòria. Aquests aspectes són: i) sistema de memòria per a un únic nucli amb patrons regulars, ii) sistema de memòria per a múltiples nuclis amb patrons regulars, iii) sistema de memòria per a un únic nucli amb patrons irregulars, iv) sistema de memòria per a múltiples nuclis amb patrons irregulars