Trauma and the Memory of Politics

Edkins, Jenny, Trauma and the Memory of Politics (Cambridge: Cambridge University Press, 2003), pp.xvii+265 RAE2008

Habit, aggregation and long memory: Evidence from television audience data

Byers, D., Peel, D., Thomas, D. (2007). Habit, aggregation and long memory: Evidence from television audience data. Applied Economics, 39 (3), 321-327. RAE2008

African Pasts: Memory and History in African Literatures

Woods, T. (2007). African Pasts: Memory and History in African Literatures. Manchetser: Manchester University Press. RAE2008

In Comes I: Performance, Memory and Landscape

Pearson, Mike, In Comes I: Performance, Memory and Landscape (Exeter: University of Exeter Press, 2007) RAE2008

New Approaches to Social History. Myth, Memory and Place: Monmouth and Bath 1750-1900

Borsay, Peter, 'New Approaches to Social History. Myth, Memory and Place: Monmouth and Bath 1750-1900', Journal of Social History (2006) 39(3) pp.867-889 RAE2008

The function of the self-attention network

This commentary links Humphrey and Sui’s proposed Self-attention Network (SAN) to the memory advantage associated with self-relevant information (i.e., the self-reference effect). Articulating this link elucidates the functional quality of the SAN in ensuring that information of potential importance to self is not lost. This adaptive system for self-processing mirrors the cognitive response to threat stimuli, which also elicit attentional biases and produce characteristically enhanced, episodic representations in memory. Understanding the link between the SAN and memory is key to comprehending more broadly the operation of the self in cognition.

A Chosen Witness: A Sermon Preached in St. Stephen's Church in Memory of the Rev. Webster

Memorial Sermon preached in memory of the Rev. Walter Gardner Webster

Tribute to the Memory of President Fisk

Tribute to the Memory of President Fisk.

Mermera: Non-Coherent Distributed Shared Memory for Parallel Computing

The proliferation of inexpensive workstations and networks has prompted several researchers to use such distributed systems for parallel computing. Attempts have been made to offer a shared-memory programming model on such distributed memory computers. Most systems provide a shared-memory that is coherent in that all processes that use it agree on the order of all memory events. This dissertation explores the possibility of a significant improvement in the performance of some applications when they use non-coherent memory. First, a new formal model to describe existing non-coherent memories is developed. I use this model to prove that certain problems can be solved using asynchronous iterative algorithms on shared-memory in which the coherence constraints are substantially relaxed. In the course of the development of the model I discovered a new type of non-coherent behavior called Local Consistency. Second, a programming model, Mermera, is proposed. It provides programmers with a choice of hierarchically related non-coherent behaviors along with one coherent behavior. Thus, one can trade-off the ease of programming with coherent memory for improved performance with non-coherent memory. As an example, I present a program to solve a linear system of equations using an asynchronous iterative algorithm. This program uses all the behaviors offered by Mermera. Third, I describe the implementation of Mermera on a BBN Butterfly TC2000 and on a network of workstations. The performance of a version of the equation solving program that uses all the behaviors of Mermera is compared with that of a version that uses coherent behavior only. For a system of 1000 equations the former exhibits at least a 5-fold improvement in convergence time over the latter. The version using coherent behavior only does not benefit from employing more than one workstation to solve the problem while the program using non-coherent behavior continues to achieve improved performance as the number of workstations is increased from 1 to 6. This measurement corroborates our belief that non-coherent shared memory can be a performance boon for some applications.

An Implementation of Mermera: A Shared Memory System that Mixes Coherence with Non-coherence

Coherent shared memory is a convenient, but inefficient, method of inter-process communication for parallel programs. By contrast, message passing can be less convenient, but more efficient. To get the benefits of both models, several non-coherent memory behaviors have recently been proposed in the literature. We present an implementation of Mermera, a shared memory system that supports both coherent and non-coherent behaviors in a manner that enables programmers to mix multiple behaviors in the same program[HS93]. A programmer can debug a Mermera program using coherent memory, and then improve its performance by selectively reducing the level of coherence in the parts that are critical to performance. Mermera permits a trade-off of coherence for performance. We analyze this trade-off through measurements of our implementation, and by an example that illustrates the style of programming needed to exploit non-coherence. We find that, even on a small network of workstations, the performance advantage of non-coherence is compelling. Raw non-coherent memory operations perform 20-40~times better than non-coherent memory operations. An example application program is shown to run 5-11~times faster when permitted to exploit non-coherence. We conclude by commenting on our use of the Isis Toolkit of multicast protocols in implementing Mermera.

Communicable Memory and Lazy Barriers for Bulk Synchronous Parallelism in BSPk

Communication and synchronization stand as the dual bottlenecks in the performance of parallel systems, and especially those that attempt to alleviate the programming burden by incurring overhead in these two domains. We formulate the notions of communicable memory and lazy barriers to help achieve efficient communication and synchronization. These concepts are developed in the context of BSPk, a toolkit library for programming networks of workstations|and other distributed memory architectures in general|based on the Bulk Synchronous Parallel (BSP) model. BSPk emphasizes efficiency in communication by minimizing local memory-to-memory copying, and in barrier synchronization by not forcing a process to wait unless it needs remote data. Both the message passing (MP) and distributed shared memory (DSM) programming styles are supported in BSPk. MP helps processes efficiently exchange short-lived unnamed data values, when the identity of either the sender or receiver is known to the other party. By contrast, DSM supports communication between processes that may be mutually anonymous, so long as they can agree on variable names in which to store shared temporary or long-lived data.

An Efficient User-Level Shared Memory Mechanism for Application-Specific Extensions

This paper focuses on an efficient user-level method for the deployment of application-specific extensions, using commodity operating systems and hardware. A sandboxing technique is described that supports multiple extensions within a shared virtual address space. Applications can register sandboxed code with the system, so that it may be executed in the context of any process. Such code may be used to implement generic routines and handlers for a class of applications, or system service extensions that complement the functionality of the core kernel. Using our approach, application-specific extensions can be written like conventional user-level code, utilizing libraries and system calls, with the advantage that they may be executed without the traditional costs of scheduling and context-switching between process-level protection domains. No special hardware support such as segmentation or tagged translation look-aside buffers (TLBs) is required. Instead, our ``user-level sandboxing'' mechanism requires only paged-based virtual memory support, given that sandboxed extensions are either written by a trusted source or are guaranteed to be memory-safe (e.g., using type-safe languages). Using a fast method of upcalls, we show how our mechanism provides significant performance improvements over traditional methods of invoking user-level services. As an application of our approach, we have implemented a user-level network subsystem that avoids data copying via the kernel and, in many cases, yields far greater network throughput than kernel-level approaches.

Cuckoo: a Language for Implementing Memory- and Thread-safe System Services

This paper is centered around the design of a thread- and memory-safe language, primarily for the compilation of application-specific services for extensible operating systems. We describe various issues that have influenced the design of our language, called Cuckoo, that guarantees safety of programs with potentially asynchronous flows of control. Comparisons are drawn between Cuckoo and related software safety techniques, including Cyclone and software-based fault isolation (SFI), and performance results suggest our prototype compiler is capable of generating safe code that executes with low runtime overheads, even without potential code optimizations. Compared to Cyclone, Cuckoo is able to safely guard accesses to memory when programs are multithreaded. Similarly, Cuckoo is capable of enforcing memory safety in situations that are potentially troublesome for techniques such as SFI.

From Working Memory to Long-term Memory and Back: Linked but Distinct

Neural models have proposed how short-term memory (STM) storage in working memory and long-term memory (LTM) storage and recall are linked and interact, but are realized by different mechanisms that obey different laws. The authors' data can be understood in the light of these models, which suggest that the authors may have gone too far in obscuring the differences between these processes.

A Memory Model for Concept Hierarchy Representation and Commonsense Reasoning

Most associative memory models perform one level mapping between predefined sets of input and output patterns1 and are unable to represent hierarchical knowledge. Complex AI systems allow hierarchical representation of concepts, but generally do not have learning capabilities. In this paper, a memory model is proposed which forms concept hierarchy by learning sample relations between concepts. All concepts are represented in a concept layer. Relations between a concept and its defining lower level concepts, are chunked as cognitive codes represented in a coding layer. By updating memory contents in the concept layer through code firing in the coding layer, the system is able to perform an important class of commonsense reasoning, namely recognition and inheritance.