BLOB computing

Abstract

Current processor and multiprocessor architectures are almost all based on the Von Neumann paradigm. Based on this paradigm, one can build a general-purpose computer using very few transistors, e.g., 2250 transistors in the first Intel 4004 microprocessor. In other terms, the notion that on-chip space is a scarce resource is at the root of this paradigm which trades on-chip space for program execution time. Today, technology considerably relaxed this space constraint. Still, few research works question this paradigm as the most adequate basis for high-performance computers, even though the paradigm was not initially designed to scale with technology and space.In this article, we propose a different computing model, defining both an architecture and a language, that is intrinsically designed to exploit space; we then investigate the implementation issues of a computer based on this model, and we provide simulation results for small programs and a simplified architecture as a first proof of concept. Through this model, we also want to outline that revisiting some of the principles of today's computing paradigm has the potential of overcoming major limitations of current architectures.

Spatial Computing as Intensional Data Parallelism

“…Others examples, including advanced cellular automata similar to those used in blob computing [48] have been developed.…”

A novel and robust axis extraction method

“…3. The general meaning of blob is binary large object [6]. Here we expand its category to grayscale.…”

CAPSULE: Hardware-Assisted Parallel Execution of Component-Based Programs

“…This model is significantly different from the Von Neumann model (processor/memory view) implicitly used by all programmers. It is more amenable to parallelism (two components residing in the space at the same time and not communicating implicitly execute concurrently), and shares properties with spatial computing models such as Blob Computing [12]. This implicit model can somewhat help the programmer for the task of extracting parallelism, though just rewriting a program into components is usually not enough to parallelize it.…”