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, IBM announced the start of a five-year effort to build a massively parallel computer, to be applied to the study of biomolecular phenomena such as protein folding. The project has two main goals: to advance our understanding of the mechanisms behind protein folding via large-scale simulation, and to explore novel ideas in massively parallel machine architecture and software. This project should enable biomolecular simulations that are orders of magnitude larger than current technology permits. Major areas of investigation include: how to most effectively utilize this novel platform to meet our scientific goals, how to make such massively parallel machines more usable, and how to achieve performance targets, with reasonable cost, through novel machine architectures. This paper provides an overview of the Blue Gene project at IBM Research. It includes some of the plans that have been made, the intended goals, and the anticipated challenges regarding the scientific work, the software application, and the hardware design.

The GraphBLAS standard (GraphBlas.org) is being developed to bring the potential of matrix-based graph algorithms to the broadest possible audience. Mathematically, the GraphBLAS defines a core set of matrix-based graph operations that can be used to implement a wide class of graph algorithms in a wide range of programming environments. This paper provides an introduction to the mathematics of the GraphBLAS. Graphs represent connections between vertices with edges. Matrices can represent a wide range of graphs using adjacency matrices or incidence matrices. Adjacency matrices are often easier to analyze while incidence matrices are often better for representing data. Fortunately, the two are easily connected by matrix multiplication. A key feature of matrix mathematics is that a very small number of matrix operations can be used to manipulate a very wide range of graphs. This composability of a small number of operations is the foundation of the GraphBLAS. A standard such as the GraphBLAS can only be effective if it has low performance overhead. Performance measurements of prototype GraphBLAS implementations indicate that the overhead is low.

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The POWER8i processor is the latest RISC (Reduced Instruction Set Computer) microprocessor from IBM. It is fabricated using the company's 22-nm Silicon on Insulator (SOI) technology with 15 layers of metal, and it has been designed to significantly improve both single-thread performance and single-core throughput over its predecessor, the POWER7 A processor. The rate of increase in processor frequency enabled by new silicon technology advancements has decreased dramatically in recent generations, as compared to the historic trend. This has caused many processor designs in the industry to show very little improvement in either single-thread or single-core performance, and, instead, larger numbers of cores are primarily pursued in each generation. Going against this industry trend, the POWER8 processor relies on a much improved core and nest microarchitecture to achieve approximately one-and-a-half times the single-thread performance and twice the single-core throughput of the POWER7 processor in several commercial applications. Combined with a 50% increase in the number of cores (from 8 in the POWER7 processor to 12 in the POWER8 processor), the result is a processor that leads the industry in performance for enterprise workloads. This paper describes the core microarchitecture innovations made in the POWER8 processor that resulted in these significant performance benefits.