Data dependences (data flow constraints) present a major hurdle to the amount of instruction-level parallelism that can be exploited from a program. Recent work has suggested that the limits imposed by data dependences can be overcome to some extent with the use of data value prediction. That is, when an instruction is fetched, its result can be predicted so that subsequent instructions that depend on the result can use this pre- dicted value. l/Vhen the correct result becomes avail- able, all instructions that are data dependent on that prediction can be validated. This paper investigates a variety of techniques to carry out highly accurate data value predictions. The first technique investigates the potential of monitoring the strides by which the results produced by different instances of an instruction change. The second technique investigates the potential of pattern-based two-level prediction schemes. Simulation results of these two schemes show improvements over the existing method of predicting the last outcome. In particular, some benchmarks show improvement with the stride-based predictor and others show improvement with the pattern-based predictor. To do uniformly well across benchmarks, we combine these two predictors to form a hybrid predictor. Simulation analysis of the hybrid predictor shows its overall prediction accuracy to be better than that of the component predictors across all benchmarks.

This paper speculates that technology trends pose new challenges for fault tolerance in microprocessors. Specifically, severely reduced design tolerances implied by gigaherz clock rates may result in frequent and arbitrary transient faults. We suggest that existing fault-tolerant techniques -- system-level, gate-level, or component-specific approaches -- are either too costly for general purpose computing, overly intrusive to the design, or insufficient for covering arbitrary logic faults. An approach in which the microarchitecture itself provides fault tolerance is required. We propose a new time redundancy fault-tolerant approach in which a program is duplicated and the two redundant programs simultaneously run on the processor. The technique exploits several significant microarchitectural trends to provide broad coverage of transient faults and restricted coverage of permanent faults. These trends are simultaneous multithreading, control flow and data flow prediction, and hierarchi...

We present an architecture that features dynamic multithreading execution of a single program. Threads are created automatically by hardware at procedure and loop boundaries and executed speculatively on a simultaneous multithreading pipeline. Data prediction is used to alleviate dependency constraints and enable lookahead execution of the threads. A two-level hierarchy significantly enlarges the instruction window. Efficient selective recovery from the second level instruction window takes place after a mispredicted input to a thread is corrected. The second level is slower to access but has the advantage of large storage capacity. We show several advantages of this architecture: (1) it minimizes the impact of ICache misses and branch mispredictions by fetching and dispatching instructions out-of-order, (2) it uses a novel value prediction and recovery mechanism to reduce artificial data dependencies created by the use of a stack to manage run-time storage, and (3) it improves the execution throughput of a superscalar by 15% without increasing the execution resources or cache bandwidth, and by 30% with one additional ICache fetch port. The speedup was measured on the integer SPEC95 benchmarks, without any compiler support, using a detailed performance simulator.

Register renaming and out-of-order instruction issue are now commonly used in superscalar processors. These techniques can also be used to significant advantage in vector processors, as this paper shows. Performance is improved and available memory bandwidth is used more effectively. Using a trace driven simulation we compare a conventional vector implementation, based on the Convex C3400, with an out-of-order, register renaming, vector implementation. When the number of physical registers is above 12, out-of-order execution coupled with register renaming provides a speedup of 1.24--1.72 for realistic memory latencies. Out-of-order techniques also tolerate main memory latencies of 100 cycles with a performance degradation less than 6%. The mechanisms used for register renaming and out-of-order issue can be used to support precise interrupts -- generally a difficult problem in vector machines. When precise interrupts are implemented, there is typically less than a 10% degradation in performance. A new technique based on register renaming is targeted at dynamically eliminating spill code; this technique is shown to provide an extra speedup ranging between 1.10 and 1.20 while reducing total memory traffic by an average of 15--20%.

This paper describes a computer architecture, Spatial Computation (SC), which is based on the translation of high-level language programs directly into hardware structures. SC program implementations are completely distributed, with no centralized control. SC circuits are optimized for wires at the expense of computation units. In this paper we investigate a particular implementation of SC: ASH (Application-Specific Hardware). Under the assumption that computation is cheaper than communication, ASH replicates computation units to simplify interconnect, building a system which uses very simple, completely dedicated communication channels. As a consequence, communication on the datapath never requires arbitration; the only arbitration required is for accessing memory. ASH relies on very simple hardware primitives, using no associative structures, no multiported register files, no scheduling logic, no broadcast, and no clocks. As a consequence, ASH hardware is fast and extremely power efficient.

Over the past twenty years, processor designers have concentrated on superscalar and VLIW architectures that exploit the instruction-level parallelism (ILP) available in engineering applications for workstation systems. Recently, however, the focus in computing has shifted from engineering to multimedia applications and from workstations to embedded systems. In this new computing environment, the performance, energy consumption, and development cost of ILP processors renders them ineffective despite their theoretical generality. This thesis

We present a complete toolflow that translates ANSI-C programs into asynchronous circuits. The toolflow is built around a compiler that converts C into a functional dataflow intermediate representation, exposing instruction-level, pipeline and memory parallelism.

As the complexity of processor architectures increases, there is a widening gap between peak processor performance and sustained processor performance so that programs now tend to exploit only a fraction of available performance. While there is a tremendous amount of literature on program optimizations, compiler optimizations lack efficiency because they are plagued by three flaws: (1) they often implicitly use simplified, if not simplistic, models of processor architecture, (2) they usually focus on a single processor component (e.g., cache) and ignore the interactions among multiple components, (3) the most heavily investigated components (e.g., caches) sometimes have only a small impact on overall performance. Through the in-depth analysis of a simple program kernel, we want to show that understanding the complex interactions between programs and the numerous processor architecture components is both feasible and critical to design efficient program optimizations. I.

Abstract. The aim of GRID superscalar is to reduce the development complexity of Grid applications to the minimum, in such a way that writing an application for a computational Grid may be as easy as writing a sequential application. Our assumption is that Grid applications would be in a lot of cases composed of tasks, most of them repetitive. The granularity of these tasks will be of the level of simulations or programs, and the data objects will be files. GRID superscalar allows application developers to write their application in a sequential fashion. The requirements to run that sequential application in a computational Grid are the specification of the interface of the tasks that should be run in the Grid, and, at some points, calls to the GRID superscalar interface functions and link with the run-time library. GRID superscalar provides an underlying run-time that is able to detect the inherent parallelism of the sequential application and performs concurrent task submission. In addition to a data-dependence analysis based on those input/output task parameters which are files, techniques such as file renaming and file locality are applied to increase the application performance. This paper presents the current GRID superscalar prototype based on Globus Toolkit 2.x, together with examples and performance evaluation of some benchmarks.

Current processors are programmed through a fixed interface called the Instruction Set Architecture (ISA). Consequently, a compiler targeting such a processor is forced to choose instructions from the provided instruction set while generating code for a given application. Often this instruction set is not a suitable match for the computational requirements of the application program. With in this context, we ask ourselves the following questions. 1. Can application performance be improved if the compiler had the freedom to pick the instruction set on a per application basis? 2. Can we build cost-effective processors that provide the ability to efficiently emulate compiler determined instruction sets and yet are not application specific? 3. Given that the desired processor capabilities are feasible, can the compiler determine an optimal set of instructions for a given application and generate code that can effectively exploit the processor capabilities? In this thesis, we provide sufficient evidence to answer these questions in the affirmative. Through a combination of architectural innovations and novel compilation techniques, this dissertation demonstrates that it is possible to attain significant improvement in performance, up to an order of magnitude in some cases, on general purpose and multimedia applications over comparable fixed ISA processors. We propose classes of microprocessors that allow application programs to add and subtract functional units yielding a dynamically varying instruction set interface to the running application without compromising current compatibility model. First half of this dissertation describes this novel class of architectures, focusing on a specific subclass called Adaptive Explicitly Parallel Instruction Computing (AEPIC) architectures...