Aces4 is a parallel programming platform comprising a DSL for Computational Chemistry and its runtime system. It offers a convenient way to express parallelism together with extensive support for extremely large, possibly sparse, distributed arrays. It aids scientists in the creation of performant, scalable, massively parallel programs that can effectively take advantage of leadership class computing systems to address important scientific questions. Aces4 has enabled the development and implementation of new methods in electronic structure theory which are breaking new ground in their ability to perform highly accurate calculations on ever larger molecular systems. In this paper the design of Aces4, which is based on the the Super Instruction Architecture approach, is described. Experimental scaling results for Molecular Cluster Perturbation Theory, a new method enabled by Aces4, and CCSD, a widely used computational chemistry method are given.

Important classes of problems in computational chemistry, notably coupled cluster methods, consist of solutions to complicated expressions defined in terms of tensors. Tensors are represented by multidimensional arrays that are typically extremely large, thus requiring distribution or in some cases backing on disk. We describe a parallel programming environment, the Super Instruction Architecture (SIA) comprising a domain specific programming language SIAL and its runtime system SIP that are specialized for this class of problems. A novel feature of the programming language is that SIAL programmers express algorithms in terms of operations on blocks rather than individual floating point numbers. Efficient implementations of the block operations as well as management of memory, communication, and I/O are provided by the runtime system. The system has been successfully used to develop ACES III, a software package for computational chemistry.

The Super Instruction Architecture (SIA) was developed to support parallel implementation of algorithms for electronic structure computational chemistry calculations. The methods are programmed in a domain specific programming language called Super Instruction Assembly Language (SIAL). An important novel aspect of SIAL is that algorithms are expressed in terms of operations (super instructions) on blocks (super numbers) rather than individual floating point numbers. The bytecode from compiled SIAL programs is executed by a parallel virtual machine known as the Super Instruction Processor (SIP). Compute intensive operations such as tensor contractions and diagonalizations, as well as communication and I/O are handled by the SIP. By separating the algorithmic complexity of the application domain in SIAL from the complexities of parallel execution on computer hardware in the SIP, a software system has been created that allows for very effective optimization and tuning on different hardware architectures with quite manageable effort.