578 research outputs found

    Architectural Approaches For Gallium Arsenide Exploitation In High-Speed Computer Design

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    Continued advances in the capability of Gallium Arsenide (GaAs)technology have finally drawn serious interest from computer system designers. The recent demonstration of very large scale integration (VLSI) laboratory designs incorporating very fast GaAs logic gates herald a significant role for GaAs technology in high-speed computer design:1 In this thesis we investigate design approaches to best exploit this promising technology in high-performance computer systems. We find significant differences between GaAs and Silicon technologies which are of relevance for computer design. The advantage that GaAs enjoys over Silicon in faster transistor switching speed is countered by a lower transistor count capability for GaAs integrated circuits. In addition, inter-chip signal propagation speeds in GaAs systems do not experience the same speedup exhibited by GaAs transistors; thus, GaAs designs are penalized more severely by inter-chip communication. The relatively low density of GaAs chips and the high cost of communication between them are significant obstacles to the full exploitation of the fast transistors of GaAs technology. A fast GaAs processor may be excessively underutilized unless special consideration is given to its information (instructions and data) requirements. Desirable GaAs system design approaches encourage low hardware resource requirements, and either minimize the processor’s need for off-chip information, maximize the rate of off-chip information transfer, or overlap off-chip information transfer with useful computation. We show the impact that these considerations have on the design of the instruction format, arithmetic unit, memory system, and compiler for a GaAs computer system. Through a simulation study utilizing a set of widely-used benchmark programs, we investigate several candidate instruction pipelines and candidate instruction formats in a GaAs environment. We demonstrate the clear performance advantage of an instruction pipeline based upon a pipelined memory system over a typical Silicon-like pipeline. We also show the performance advantage of packed instruction formats over typical Silicon instruction formats, and present a packed format which performs better than the experimental packed Stanford MIPS format

    Space Station Freedom data management system growth and evolution report

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    The Information Sciences Division at the NASA Ames Research Center has completed a 6-month study of portions of the Space Station Freedom Data Management System (DMS). This study looked at the present capabilities and future growth potential of the DMS, and the results are documented in this report. Issues have been raised that were discussed with the appropriate Johnson Space Center (JSC) management and Work Package-2 contractor organizations. Areas requiring additional study have been identified and suggestions for long-term upgrades have been proposed. This activity has allowed the Ames personnel to develop a rapport with the JSC civil service and contractor teams that does permit an independent check and balance technique for the DMS

    Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks

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    Predicting the number of clock cycles a processor takes to execute a block of assembly instructions in steady state (the throughput) is important for both compiler designers and performance engineers. Building an analytical model to do so is especially complicated in modern x86-64 Complex Instruction Set Computer (CISC) machines with sophisticated processor microarchitectures in that it is tedious, error prone, and must be performed from scratch for each processor generation. In this paper we present Ithemal, the first tool which learns to predict the throughput of a set of instructions. Ithemal uses a hierarchical LSTM--based approach to predict throughput based on the opcodes and operands of instructions in a basic block. We show that Ithemal is more accurate than state-of-the-art hand-written tools currently used in compiler backends and static machine code analyzers. In particular, our model has less than half the error of state-of-the-art analytical models (LLVM's llvm-mca and Intel's IACA). Ithemal is also able to predict these throughput values just as fast as the aforementioned tools, and is easily ported across a variety of processor microarchitectures with minimal developer effort.Comment: Published at 36th International Conference on Machine Learning (ICML) 201

    Functional requirements document for the Earth Observing System Data and Information System (EOSDIS) Scientific Computing Facilities (SCF) of the NASA/MSFC Earth Science and Applications Division, 1992

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    Five scientists at MSFC/ESAD have EOS SCF investigator status. Each SCF has unique tasks which require the establishment of a computing facility dedicated to accomplishing those tasks. A SCF Working Group was established at ESAD with the charter of defining the computing requirements of the individual SCFs and recommending options for meeting these requirements. The primary goal of the working group was to determine which computing needs can be satisfied using either shared resources or separate but compatible resources, and which needs require unique individual resources. The requirements investigated included CPU-intensive vector and scalar processing, visualization, data storage, connectivity, and I/O peripherals. A review of computer industry directions and a market survey of computing hardware provided information regarding important industry standards and candidate computing platforms. It was determined that the total SCF computing requirements might be most effectively met using a hierarchy consisting of shared and individual resources. This hierarchy is composed of five major system types: (1) a supercomputer class vector processor; (2) a high-end scalar multiprocessor workstation; (3) a file server; (4) a few medium- to high-end visualization workstations; and (5) several low- to medium-range personal graphics workstations. Specific recommendations for meeting the needs of each of these types are presented

    Empirical and Statistical Application Modeling Using on -Chip Performance Monitors.

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    To analyze the performance of applications and architectures, both programmers and architects desire formal methods to explain anomalous behavior. To this end, we present various methods that utilize non-intrusive, performance-monitoring hardware only recently available on microprocessors to provide further explanations of observed behavior. All the methods attempt to characterize and explain the instruction-level parallelism achieved by codes on different architectures. We also present a prototype tool automating the analysis process to exploit the advantages of the empirical and statistical methods proposed. The empirical, statistical and hybrid methods are discussed and explained with case study results provided. The given methods further the wealth of tools available to programmer\u27s and architects for generally understanding the performance of scientific applications. Specifically, the models and tools presented provide new methods for evaluating and categorizing application performance. The empirical memory model serves to quantify the hierarchical memory performance of applications by inferring the incurred latencies of codes after the effect of latency hiding techniques are realized. The instruction-level model and its extensions model on-chip performance analytically giving insight into inherent performance bottlenecks in superscalar architectures. The statistical model and its hybrid extension provide other methods of categorizing codes via their statistical variations. The PTERA performance tool automates the use of performance counters for use by these methods across platforms making the modeling process easier still. These unique methods provide alternatives to performance modeling and categorizing not available previously in an attempt to utilize the inherent modeling capabilities of performance monitors on commodity processors for scientific applications

    Hierarchical architecture design and simulation environment

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    The Hierarchical Architectural design and Simulation Environment (HASE)is intended as a flexible tool for computer architects who wish to experiment with alternative architectural configurations and design parameters. HASE is both a design environment and a simulator. Architecture components are described by a hierarchical library of objects defined in terms of an object oriented simulation language. HASE instantiates these objects to simulate and animate the execution of a computer architecture. An event trace generated by the simulator therefore describes the interaction between architecture components, for example, fetch stages, address and data buses, sequencers, instruction buffers and register files. The objects can model physical components at different abstraction levels, eg. PMS (processor memory switch), ISP (instruction set processor) and RTL (register transfer level). HASE applies the concepts of inheritance, encapsulation and polymorphism associated with object orientation, to simplify the design and implementation of an architecture simulation that models component operations at different abstraction levels. For example, HASE can probe the performance of a processor's floating point unit, executing a multiplication operation, at a lower level of abstraction, i.e. the RTL, whilst simulating remaining architecture components at a PMS level of abstraction. By adopting this approach, HASE returns a more meaningful and relevant event trace from an architecture simulation. Furthermore, an animator visualises the simulation's event trace to clarify the collaborations and interactions between architecture components. The prototype version of HASE is based on GSS (Graphical Support System), and DEMOS (Discrete Event Modelling On Simula)

    ret2spec: Speculative Execution Using Return Stack Buffers

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    Speculative execution is an optimization technique that has been part of CPUs for over a decade. It predicts the outcome and target of branch instructions to avoid stalling the execution pipeline. However, until recently, the security implications of speculative code execution have not been studied. In this paper, we investigate a special type of branch predictor that is responsible for predicting return addresses. To the best of our knowledge, we are the first to study return address predictors and their consequences for the security of modern software. In our work, we show how return stack buffers (RSBs), the core unit of return address predictors, can be used to trigger misspeculations. Based on this knowledge, we propose two new attack variants using RSBs that give attackers similar capabilities as the documented Spectre attacks. We show how local attackers can gain arbitrary speculative code execution across processes, e.g., to leak passwords another user enters on a shared system. Our evaluation showed that the recent Spectre countermeasures deployed in operating systems can also cover such RSB-based cross-process attacks. Yet we then demonstrate that attackers can trigger misspeculation in JIT environments in order to leak arbitrary memory content of browser processes. Reading outside the sandboxed memory region with JIT-compiled code is still possible with 80\% accuracy on average.Comment: Updating to the cam-ready version and adding reference to the original pape
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