Performance modeling is becoming an increasingly important part of the parallel application development process, particularly for expensive computations that will be run on very high-end systems where resources are scarce. We describe a performance modeling tool SIPMaP (Super Instruction Processor Modeling and Prediction) developed for the Super-Instruction Architecture (SIA). The SIA is designed for applications where the dominant data structures are large multi-dimensional arrays and it comprises a DSL, the Super-Instruction Assembly Language (SIAL) that supports expressing algorithms in terms of blocks (tiles), and its runtime system Super Instruction Processor (SIP) that manages distribution and disk storage of the arrays. SIPMaP generates performance models from the SIAL source code. In comparison with many applications where useful performance models have been developed and reported, these programs are irregular and have other difficult to model characteristics such as extensive overlapping of communication and computation.

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.