Molecular dynamics (MD) simulations are widely used to study protein motions at an atomic level of detail, but they have been limited to time scales shorter than those of many biologically critical conformational changes. We examined two fundamental processes in protein dynamics--protein folding and conformational change within the folded state--by means of extremely long all-atom MD simulations conducted on a special-purpose machine. Equilibrium simulations of a WW protein domain captured multiple folding and unfolding events that consistently follow a well-defined folding pathway; separate simulations of the protein's constituent substructures shed light on possible determinants of this pathway. A 1-millisecond simulation of the folded protein BPTI reveals a small number of structurally distinct conformational states whose reversible interconversion is slower than local relaxations within those states by a factor of more than 1000.

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Anton, a massively parallel special-purpose machine for molecular dynamics simulations, performs a 32×32×32 FFT in 3.7 microseconds and a 64×64×64 FFT in 13.3 microseconds on a configuration with 512 nodes-an order of magnitude faster than all other FFT implementations of which we are aware. Achieving this FFT performance requires a coordinated combination of computation and communication techniques that leverage Anton's underlying hardware mechanisms. Most significantly, Anton's communication subsystem provides over 300 gigabits per second of bandwidth per node, message latency in the hundreds of nanoseconds, and support for word-level writes and single-ended communication. In addition, Anton's general-purpose computation system incorporates primitives that support the efficient parallelization of small 1D FFTs. Although Anton was designed specifically for molecular dynamics simulations, a number of the hardware primitives and software implementation techniques described in this paper may also be applicable to the acceleration of FFTs on general-purpose high-performance machines.

Java offers the real possibility that most programs can be written in a type-safe language. However, for Java to be broadly useful, it needs additional expressive power. This paper extends Java in one area where more power is needed: support for parametric polymorphism, which allows the definition and implementation of generic abstractions. The paper discusses both the rationale for our design decisions and the impact of the extension on other parts of Java, including arrays and the class library. It also describes an implementation of the mechanism, including extensions to the Java virtual machine, and designs for the bytecode verifier and interpreter. The bytecode interpreter has been implemented; it provides good performance for parameterized code in both execution speed and code size, and does not slow down programs that do not use parameterized code.

Abstract-Special-purpose computing hardware can provide significantly better performance and power efficiency for certain applications than general-purpose processors. Even within a single application area, however, a special-purpose machine can be far more valuable if it is capable of efficiently supporting a number of different computational methods that, taken together, expand the machine's functionality and range of applicability. We have previously described a massively parallel special-purpose supercomputer, called Anton, and have shown that it executes traditional molecular dynamics simulations orders of magnitude faster than the previous state of the art. Here, we describe how we extended Anton's software to support a more diverse set of methods, allowing scientists to simulate a broader class of biological phenomena at extremely high speeds. Key elements of our approach, which exploits Anton's tightly integrated hardwired pipelines and programmable cores, are applicable to the hardware and software design of various other specialized or heterogeneous parallel computing platforms.

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