Highly parallel computing architectures are the only means to achieve the computational rates demanded by advanced scientific problems. A decade of research has demonstrated the feasibility of such machines, and current research focuses on which architectures are best suited for particular classes of problems. The architectures designated as MIMD and SIMD have produced the best results to date; neither shows a decisive advantage for most near-homogeneous scientific problems. For scientific problems with many dissimilar parts, more speculative architectures such as neural networks or data flow may be needed.