We present the design of Brown University's new Cave, which is expected to be fully operational in February 2014. With one arc-minute resolution, 3.8 π steradians of visual surround, head-tracked stereo, and an almost seamless screen, this Cave offers advances to the state-of-the-art virtual reality experience. This improvement is achieved with the installation of 69 high-resolution long throw projectors, a cylindrical screen with conical ceiling, and a 135 square foot rear-projection floor. Though Caves have been around for over 20 years, they have remained impractical for many potential uses due to their limited resolution, brightness, and overall immersion. Brown's new Cave aims to bridge this gap.

The goal of High Performance Fortran (HPF) is to "address the problems of writing data parallel programs where the distribution of data affects performance",

Data files for scientific and engineering codes typically consist of a series of raw data values whose description is buried in the programs that interact with these files. In this situation, making even minor changes in the file structure or sharing files between programs (interoperability) can only be done after careful examination of the data files and the I/O statements of the programs interacting with this file. In short, scientific data files lack self-description, and other self-describing data techniques are not always appropriate or useful for scientific data files. By applying an object-oriented methodology to data files, we can add the intelligence required to improve data interoperability and provide an elegant mechanism for supporting complex, evolving, or multidisciplinary applications, while still supporting legacy codes. As a result, scientists and engineers should be able to share datasets with far greater ease, simplifying multidisciplinary applications and greatly facilitating remote collaboration between scientists.

Vienna Fortran supports a wide range of data-parallel numerical problems. However, a significant number of scientific and engineering applications are of a multi-disciplinary and heterogeneous nature and thus do not fit well into the data parallel paradigm. In this paper we present new language extensions to fill this gap. Tasks can be spawned as asynchronous activities in a homogeneous or heterogeneous computing environment; they interact by sharing access to Shared Data Abstractions (SDAs). SDAs are an extension of Fortran 90 modules, representing a pool of common data, together with a set of methods for controlled access to these data and a mechanism for providing persistent storage. These extensions support the integration of data and task parallelism and can be used to express task parallel applications in a natural and efficient way.

Programming nonshared memory systems is more difficult than programming shared memory systems, since there is no support for shared data structures. Current programming languages for distributed memory architectures force the user to decompose all data structures into separate pieces, with each piece “owned” by one of the processors in the machine, and with all communication explicitly specified by low-level message-passing primitives. This paper presents a new programming environment for distributed memory architectures, providing a global name space and allowing direct access to remote parts of data values. We describe the analysis and program transformations required to implement this environment, and present the efficiency of the resulting code on the NCUBE/7 and IPSC/2 hypercubes.

Distributed memory architectures offer high levels of performance and flexibility, but have proven awkward to program. Current languages for nonshared memory architectures provide a relatively low-level programming environment, and are poorly suited to modular programming, and to the construction of libraries. This paper describes a set of language primitives designed to allow the specification of parallel numerical algorithms at a higher level. We focus here on tensor product array computations, a simple but important class of numerical algorithms. We consider first the problem of programming one dimensional “kernel” routines, such as parallel tridiagonal solvers, and after that look at how such parallel kernels can be combined to form parallel tensor product algorithms.

This paper describes two models of the cost of data movement in parallel numerical algorithms. One model is a generalization of an approach due to Hockney, and is suitable for shared memory multiprocessors where each processor has vector capabilities. The other model is applicable to highly parallel nonshared memory MIMD systems. In this second model, algorithm performance is characterized in terms of the communication network design. Techniques used in VLSI complexity theory are also brought in, and algorithm-independent upper bounds on system performance are derived for several problems that are important to scientific computation.