Dataflow computers: a tutorial and survey

Journal ArticleThe demand for very high performance computer has encouraged some researchers in the computer science field to consider alternatives to the conventional notions of program and computer organization. The dataflow computer is one attempt to form a new collection of consistent systems ideas to improve both computer performance and to alleviate the software design problems induced by the construction of highly concurrent programs

The impact of asynchrony on computer architecture

The performance characteristics of asynchronous circuits are quite different from those of their synchronous counterparts. As a result, the best asynchronous design of a particular system does not necessarily correspond to the best synchronous design, even at the algorithmic level. The goal of this thesis is to examine certain aspects of computer architecture and design in the context of an asynchronous VLSI implementation. We present necessary and sufficient conditions under which the degree of pipelining of a component can be modified without affecting the correctness of an asynchronous computation. As an instance of the improvements possible using an asynchronous architecture, we present circuits to solve the prefix problem with average-case behavior better than that possible by any synchronous solution in the case when the prefix operator has a right zero. We show that our circuit implementations are area-optimal given their performance characteristics, and have the best possible average-case latency. At the level of processor design, we present a mechanism for the implementation of precise exceptions in asynchronous processors. The novel feature of this mechanism is that it permits the presence of a data-dependent number of instructions in the execution pipeline of the processor. Finally, at the level of processor architecture, we present the architecture of a processor with an independent instruction stream for branches. The instruction set permits loops and function calls to be executed with minimal control-flow overhead

An Expressive Language and Efficient Execution System for Software Agents

Software agents can be used to automate many of the tedious, time-consuming information processing tasks that humans currently have to complete manually. However, to do so, agent plans must be capable of representing the myriad of actions and control flows required to perform those tasks. In addition, since these tasks can require integrating multiple sources of remote information ? typically, a slow, I/O-bound process ? it is desirable to make execution as efficient as possible. To address both of these needs, we present a flexible software agent plan language and a highly parallel execution system that enable the efficient execution of expressive agent plans. The plan language allows complex tasks to be more easily expressed by providing a variety of operators for flexibly processing the data as well as supporting subplans (for modularity) and recursion (for indeterminate looping). The executor is based on a streaming dataflow model of execution to maximize the amount of operator and data parallelism possible at runtime. We have implemented both the language and executor in a system called THESEUS. Our results from testing THESEUS show that streaming dataflow execution can yield significant speedups over both traditional serial (von Neumann) as well as non-streaming dataflow-style execution that existing software and robot agent execution systems currently support. In addition, we show how plans written in the language we present can represent certain types of subtasks that cannot be accomplished using the languages supported by network query engines. Finally, we demonstrate that the increased expressivity of our plan language does not hamper performance; specifically, we show how data can be integrated from multiple remote sources just as efficiently using our architecture as is possible with a state-of-the-art streaming-dataflow network query engine

An Ultra-Low-Power 75mV 64-Bit Current-Mode Majority-Function Adder

Ultra-low-power circuits are becoming more desirable due to growing portable device markets and they are also becoming more interesting and applicable today in biomedical, pharmacy and sensor networking applications because of the nano-metric scaling and CMOS reliability improvements. In this thesis, three main achievements are presented in ultra-low-power adders. First, a new majority function algorithm for carry and the sum generation is presented. Then with this algorithm and implied new architecture, we achieved a circuit with 75mV supply voltage operation. Last but not least, a 64 bit current-mode majority-function adder based on the new architecture and algorithm is successfully tested at 75mV supply voltage. The circuit consumed 4.5nW or 3.8pJ in one of the worst conditions

Techniques for power system simulation using multiple processors

The thesis describes development work which was undertaken to improve the speed of a real-time power system simulator used for the development and testing of control schemes. The solution of large, highly sparse matrices was targeted because this is the most time-consuming part of the current simulator. Major improvements in the speed of the matrix ordering phase of the solution were achieved through the development of a new ordering strategy. This was thoroughly investigated, and is shown to provide important additional improvements compared to standard ordering methods, in reducing path length and minimising potential pipeline stalls. Alterations were made to the remainder of the solution process which provided more flexibility in scheduling calculations. This was used to dramatically ease the run-time generation of efficient code, dedicated to the solution of one matrix structure, and also to reduce memory requirements. A survey of the available microprocessors was performed, which concluded that a special-purpose design could best implement the code generated at run-time, and a design was produced using a microprogrammable floating-point processor, which matched the code produced by the earlier work. A method of splitting the matrix solution onto parallel processors was investigated, and two methods of producing network splits were developed and their results compared. The best results from each method were found to agree well, with a predicted three-fold speed-up for the matrix solution of the C.E.G.B. transmission system from the use of six processors. This gain will increase for the whole simulator. A parallel processing topology of the partitioned network and produce the necessary structures for the remainder of the solution process

Parallelization of dynamic programming recurrences in computational biology

The rapid growth of biosequence databases over the last decade has led to a performance bottleneck in the applications analyzing them. In particular, over the last five years DNA sequencing capacity of next-generation sequencers has been doubling every six months as costs have plummeted. The data produced by these sequencers is overwhelming traditional compute systems. We believe that in the future compute performance, not sequencing, will become the bottleneck in advancing genome science. In this work, we investigate novel computing platforms to accelerate dynamic programming algorithms, which are popular in bioinformatics workloads. We study algorithm-specific hardware architectures that exploit fine-grained parallelism in dynamic programming kernels using field-programmable gate arrays: FPGAs). We advocate a high-level synthesis approach, using the recurrence equation abstraction to represent dynamic programming and polyhedral analysis to exploit parallelism. We suggest a novel technique within the polyhedral model to optimize for throughput by pipelining independent computations on an array. This design technique improves on the state of the art, which builds latency-optimal arrays. We also suggest a method to dynamically switch between a family of designs using FPGA reconfiguration to achieve a significant performance boost. We have used polyhedral methods to parallelize the Nussinov RNA folding algorithm to build a family of accelerators that can trade resources for parallelism and are between 15-130x faster than a modern dual core CPU implementation. A Zuker RNA folding accelerator we built on a single workstation with four Xilinx Virtex 4 FPGAs outperforms 198 3 GHz Intel Core 2 Duo processors. Furthermore, our design running on a single FPGA is an order of magnitude faster than competing implementations on similar-generation FPGAs and graphics processors. Our work is a step toward the goal of automated synthesis of hardware accelerators for dynamic programming algorithms

The instruction of systolic array (ISA) and simulation of parallel algorithms

Systolic arrays have proved to be well suited for Very Large Scale Integrated technology (VLSI) since they: -Consist of a regular network of simple processing cells, -Use local communication between the processing cells only, -Exploit a maximal degree of parallelism. However, systolic arrays have one main disadvantage compared with other parallel computer architectures: they are special purpose architectures only capable of executing one algorithm, e.g., a systolic array designed for sorting cannot be used to form matrix multiplication. Several approaches have been made to make systolic arrays more flexible, in order to be able to handle different problems on a single systolic array. In this thesis an alternative concept to a VLSI-architecture the Soft-Systolic Simulation System (SSSS), is introduced and developed as a working model of virtual machine with the power to simulate hard systolic arrays and more general forms of concurrency such as the SIMD and MIMD models of computation. The virtual machine includes a processing element consisting of a soft-systolic processor implemented in the virtual.machine language. The processing element considered here was a very general element which allows the choice of a wide range of arithmetic and logical operators and allows the simulation of a wide class of algorithms but in principle extra processing cells can be added making a library and this library be tailored to individual needs. The virtual machine chosen for this implementation is the Instruction Systolic Array (ISA). The ISA has a number of interesting features, firstly it has been used to simulate all SIMD algorithms and many MIMD algorithms by a simple program transformation technique, further, the ISA can also simulate the so-called wavefront processor algorithms, as well as many hard systolic algorithms. The ISA removes the need for the broadcasting of data which is a feature of SIMD algorithms (limiting the size of the machine and its cycle time) and also presents a fairly simple communication structure for MIMD algorithms. The model of systolic computation developed from the VLSI approach to systolic arrays is such that the processing surface is fixed, as are the processing elements or cells by virtue of their being embedded in the processing surface. The VLSI approach therefore freezes instructions and hardware relative to the movement of data with the virtual machine and softsystolic programming retaining the constructions of VLSI for array design features such as regularity, simplicity and local communication, allowing the movement of instructions with respect to data. Data can be frozen into the structure with instructions moving systolically. Alternatively both the data and instructions can move systolically around the virtual processors, (which are deemed fixed relative to the underlying architecture). The ISA is implemented in OCCAM programs whose execution and output implicitly confirm the correctness of the design. The soft-systolic preparation comprises of the usual operating system facilities for the creation and modification of files during the development of new programs and ISA processor elements. We allow any concurrent high level language to be used to model the softsystolic program. Consequently the Replicating Instruction Systolic Array Language (RI SAL) was devised to provide a very primitive program environment to the ISA but adequate for testing. RI SAL accepts instructions in an assembler-like form, but is fairly permissive about the format of statements, subject of course to syntax. The RI SAL compiler is adopted to transform the soft-systolic program description (RISAL) into a form suitable for the virtual machine (simulating the algorithm) to run. Finally we conclude that the principles mentioned here can form the basis for a soft-systolic simulator using an orthogonally connected mesh of processors. The wide range of algorithms which the ISA can simulate make it suitable for a virtual simulating grid