Towards compliant distributed shared memory

Copyright © 2002 IEEEThere exists a wide spectrum of coherency models for use in distributed shared memory (DSM) systems. The choice of model for an application should ideally be based on the application's data access patterns and phase changes. However, in current systems, most, if not all of the parameters of the coherency model are fixed in the underlying DSM system. This forces the application either to structure its computations to suit the underlying model or to endure an inefficient coherency model. This paper introduces a unique approach to the provision of DSM based on the idea of compliance. Compliance allows an application to specify how the system should most effectively operate through a separation between mechanism, provided by the underlying system, and policy, pro-vided by the application. This is in direct contrast with the traditional view that an application must mold itself to the hard-wired choices that its operating platform has made. The contribution of this work is the definition and implementation of an architecture for compliant distributed coherency management. The efficacy of this architecture is illustrated through a worked example.Falkner, K. E.; Detmold, H.; Munro, D. S.; Olds, T

Extending and Implementing the Self-adaptive Virtual Processor for Distributed Memory Architectures

Many-core architectures of the future are likely to have distributed memory organizations and need fine grained concurrency management to be used effectively. The Self-adaptive Virtual Processor (SVP) is an abstract concurrent programming model which can provide this, but the model and its current implementations assume a single address space shared memory. We investigate and extend SVP to handle distributed environments, and discuss a prototype SVP implementation which transparently supports execution on heterogeneous distributed memory clusters over TCP/IP connections, while retaining the original SVP programming model

Virtual Machine Support for Many-Core Architectures: Decoupling Abstract from Concrete Concurrency Models

The upcoming many-core architectures require software developers to exploit concurrency to utilize available computational power. Today's high-level language virtual machines (VMs), which are a cornerstone of software development, do not provide sufficient abstraction for concurrency concepts. We analyze concrete and abstract concurrency models and identify the challenges they impose for VMs. To provide sufficient concurrency support in VMs, we propose to integrate concurrency operations into VM instruction sets. Since there will always be VMs optimized for special purposes, our goal is to develop a methodology to design instruction sets with concurrency support. Therefore, we also propose a list of trade-offs that have to be investigated to advise the design of such instruction sets. As a first experiment, we implemented one instruction set extension for shared memory and one for non-shared memory concurrency. From our experimental results, we derived a list of requirements for a full-grown experimental environment for further research

Feed-forward volume rendering algorithm for moderately parallel MIMD machines

Algorithms for direct volume rendering on parallel and vector processors are investigated. Volumes are transformed efficiently on parallel processors by dividing the data into slices and beams of voxels. Equal sized sets of slices along one axis are distributed to processors. Parallelism is achieved at two levels. Because each slice can be transformed independently of others, processors transform their assigned slices with no communication, thus providing maximum possible parallelism at the first level. Within each slice, consecutive beams are incrementally transformed using coherency in the transformation computation. Also, coherency across slices can be exploited to further enhance performance. This coherency yields the second level of parallelism through the use of the vector processing or pipelining. Other ongoing efforts include investigations into image reconstruction techniques, load balancing strategies, and improving performance