A bibliography on parallel and vector numerical algorithms

This is a bibliography of numerical methods. It also includes a number of other references on machine architecture, programming language, and other topics of interest to scientific computing. Certain conference proceedings and anthologies which have been published in book form are listed also

Efficient parallel processing with optical interconnections

With the advances in VLSI technology, it is now possible to build chips which can each contain thousands of processors. The efficiency of such chips in executing parallel algorithms heavily depends on the interconnection topology of the processors. It is not possible to build a fully interconnected network of processors with constant fan-in/fan-out using electrical interconnections. Free space optics is a remedy to this limitation. Qualities exclusive to the optical medium are its ability to be directed for propagation in free space and the property that optical channels can cross in space without any interference. In this thesis, we present an electro-optical interconnected architecture named Optical Reconfigurable Mesh (ORM). It is based on an existing optical model of computation. There are two layers in the architecture. The processing layer is a reconfigurable mesh and the deflecting layer contains optical devices to deflect light beams. ORM provides three types of communication mechanisms. The first is for arbitrary planar connections among sets of locally connected processors using the reconfigurable mesh. The second is for arbitrary connections among N of the processors using the electrical buses on the processing layer and N2 fixed passive deflecting units on the deflection layer. The third is for arbitrary connections among any of the N2 processors using the N2 mechanically reconfigurable deflectors in the deflection layer. The third type of communication mechanisms is significantly slower than the other two. Therefore, it is desirable to avoid reconfiguring this type of communication during the execution of the algorithms. Instead, the optical reconfiguration can be done before the execution of each algorithm begins. Determining a right configuration that would be suitable for the entire configuration of a task execution is studied in this thesis. The basic data movements for each of the mechanisms are studied. Finally, to show the power of ORM, we use all three types of communication mechanisms in the first O(logN) time algorithm for finding the convex hulls of all figures in an N x N binary image presented in this thesis

Parallel simulation of neural networks on SpiNNaker universal neuromorphic hardware

Artificial neural networks have shown great potential and have attracted much research interest. One problem faced when simulating such networks is speed. As the number of neurons increases, the time to simulate and train a network increases dramatically. This makes it difficult to simulate and train a large-scale network system without the support of a high-performance computer system. The solution we present is a "real" parallel system - using a parallel machine to simulate neural networks which are intrinsically parallel applications. SpiNNaker is a scalable massively-parallel computing system under development with the aim of building a general-purpose platform for the parallel simulation of large-scale neural systems. This research investigates how to model large-scale neural networks efficiently on such a parallel machine. While providing increased overall computational power, a parallel architecture introduces a new problem - the increased communication reduces the speedup gains. Modeling schemes, which take into account communication, processing, and storage requirements, are investigated to solve this problem. Since modeling schemes are application-dependent, two different types of neural network are examined - spiking neural networks with spike-time dependent plasticity, and the parallel distributed processing model with the backpropagation learning rule. Different modeling schemes are developed and evaluated for the two types of neural network. The research shows the feasibility of the approach as well as the performance of SpiNNaker as a general-purpose platform for the simulation of neural networks. The linear scalability shown in this architecture provides a path to the further development of parallel solutions for the simulation of extremely large-scale neural networks.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Efficient Interconnection Network Design for Heterogeneous Architectures

The onset of big data and deep learning applications, mixed with conventional general-purpose programs, have driven computer architecture to embrace heterogeneity with specialization. With the ever-increasing interconnected chip components, future architectures are required to operate under a stricter power budget and process emerging big data applications efficiently. Interconnection network as the communication backbone thus is facing the grand challenges of limited power envelope, data movement and performance scaling. This dissertation provides interconnect solutions that are specialized to application requirements towards power-/energy-efficient and high-performance computing for heterogeneous architectures. This dissertation examines the challenges of network-on-chip router power-gating techniques for general-purpose workloads to save static power. A voting approach is proposed as an adaptive power-gating policy that considers both local and global traffic status through router voting. In addition, low-latency routing algorithms are designed to guarantee performance in irregular power-gating networks. This holistic solution not only saves power but also avoids performance overhead. This research also introduces emerging computation paradigms to interconnects for big data applications to mitigate the pressure of data movement. Approximate network-on-chip is proposed to achieve high-throughput communication by means of lossy compression. Then, near-data processing is combined with in-network computing to further improve performance while reducing data movement. The two schemes are general to play as plug-ins for different network topologies and routing algorithms. To tackle the challenging computational requirements of deep learning workloads, this dissertation investigates the compelling opportunities of communication algorithm-architecture co-design to accelerate distributed deep learning. MultiTree allreduce algorithm is proposed to bond with message scheduling with network topology to achieve faster and contention-free communication. In addition, the interconnect hardware and flow control are also specialized to exploit deep learning communication characteristics and fulfill the algorithm needs, thereby effectively improving the performance and scalability. By considering application and algorithm characteristics, this research shows that interconnection network can be tailored accordingly to improve the power-/energy-efficiency and performance to satisfy heterogeneous computation and communication requirements

Tightly-Coupled and Fault-Tolerant Communication in Parallel Systems

The demand for processing power is increasing steadily. In the past, single processor architectures clearly dominated the markets. As instruction level parallelism is limited in most applications, significant performance can only be achieved in the future by exploiting parallelism at the higher levels of thread or process parallelism. As a consequence, modern “processors” incorporate multiple processor cores that form a single shared memory multiprocessor. In such systems, high performance devices like network interface controllers are connected to processors and memory like every other input/output device over a hierarchy of peripheral interconnects. Thus, one target must be to couple coprocessors physically closer to main memory and to the processors of a computing node. This removes the overhead of today’s peripheral interconnect structures. Such a step is the direct connection of HyperTransport (HT) devices to Opteron processors, which is presented in this thesis. Also, this work analyzes how communication from a device to processors can be optimized on the protocol level. As today’s computing nodes are shared memory systems, the cache coherence protocol is the central protocol for data exchange between processors and devices. Consequently, the analysis extends to classes of devices that are cache coherence protocol aware. Also, the concept of a transfer cache is proposed in this thesis, which reduces latency significantly even for non-coherent devices. The trend to the exploitation of process and thread level parallelism leads to a steady increase of system sizes. Networks that are used in such large systems are very susceptible to both hard and transient faults. Most transient fault rates are constant per bit that is stored or transmitted. With increasing system sizes and higher clock frequencies, the number of faults in time increases drastically. In the end, the error rate may rise at a level where high level error recovery becomes too costly if lower layers do not perform error correction that is transparent to the layers above. The second part of this thesis describes a direct interconnection network that provides a reliable transport service even without the use of end-to-end protocols. Also, a novel hardware based solution for intermediate routing is developed in this thesis, which allows an efficient, deadlock free routing around faulty links

The connection machine

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1988.Bibliography: leaves 134-157.by William Daniel Hillis.Ph.D

The Fifth NASA Symposium on VLSI Design

The fifth annual NASA Symposium on VLSI Design had 13 sessions including Radiation Effects, Architectures, Mixed Signal, Design Techniques, Fault Testing, Synthesis, Signal Processing, and other Featured Presentations. The symposium provides insights into developments in VLSI and digital systems which can be used to increase data systems performance. The presentations share insights into next generation advances that will serve as a basis for future VLSI design

Towards hardware as a reconfigurable, elastic, and specialized service

As modern Data Center workloads become increasingly complex, constrained, and critical, mainstream CPU-centric computing has had ever more difficulty in keeping pace. Future data centers are moving towards a more fluid and heterogeneous model, with computation and communication no longer localized to commodity CPUs and routers. Next generation data-centric Data Centers will compute everywhere, whether data is stationary (e.g. in memory) or on the move (e.g. in network). While deploying FPGAs in NICS, as co-processors, in the router, and in Bump-in-the-Wire configurations is a step towards implementing the data-centric model, it is only part of the overall solution. The other part is actually leveraging this reconfigurable hardware. For this to happen, two problems must be addressed: code generation and deployment generation. By code generation we mean transforming abstract representations of an algorithm into equivalent hardware. Deployment generation refers to the runtime support needed to facilitate the execution of this hardware on an FPGA. Efforts at creating supporting tools in these two areas have thus far provided limited benefits. This is because the efforts are limited in one or more of the following ways: They i) do not provide fundamental solutions to a number of challenges, which makes them useful only to a limited group of (mostly) hardware developers, ii) are constrained in their scope, or iii) are ad hoc, i.e., specific to a single usage context, FPGA vendor, or Data Center configuration. Moreover, efforts in these areas have largely been mutually exclusive, which results in incompatibility across development layers; this requires wrappers to be designed to make interfaces compatible. As a result there is significant complexity and effort required to code and deploy efficient custom hardware for FPGAs; effort that may be orders-of-magnitude greater than for analogous software environments. The goal of this dissertation is to create a framework that enables reconfigurable logic in Data Centers to be targeted with the same level of effort as for a single CPU core. The underlying mechanism to this is a framework, which we refer to as Hardware as a Reconfigurable, Elastic and Specialized Service, or HaaRNESS. In this dissertation, we address two of the core challenges of HaaRNESS: reducing the complexity of code generation by constraining High Level Synthesis (HLS) toolflows, and replacing ad hoc models of deployment generation by generalizing and formalizing what is needed for a hardware Operating System. These parts are unified by the back-end of HLS toolflows which link generated compute pipelines with the operating system, and provide appropriate APIs, wrappers, and software runtimes. The contributions of this dissertation are the following: i) an empirically guided set of systematic transformations for generating high quality HLS code; ii) a framework for instrumenting HLS compiler to identify and remove optimization blockers; iii) a framework for RTL simulation and IP generation of HLS kernels for rapid turnaround; and iv) a framework for generalization and formalization of hardware operating systems to address the {\it ad hoc}'ness of existing deployment generation and ensure uniform structure and APIs