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Advances in computer and electrical engineering book series

Parallel Quantum Chemistry at the Crossroads

Hubertus J. J. van Dam

Published Nov 17, 2011• 0 citations
Publisher

Abstract

Quantum chemistry was a compute intensive field from the beginning. It was also an early adopter of parallel computing, and hence, has more than twenty years of experience with parallelism. However, recently parallel computing has seen dramatic changes, such as the rise of multi-core architectures, hybrid computing, and the prospect of exa-scale machines requiring 1 billion concurrent threads. It is doubtful that current approaches can address the challenges ahead. As a result, the field finds itself at a crossroads, facing the challenge to successfully identify the way forward. This chapter tells a story in two parts. First, the achievements to date are considered, offering insights learned so far. Second, we look at paradigms based on directed acyclic graphs (DAG). The computer science community is strongly advocating this paradigm, but the quantum chemistry community has no experience with this approach. Therefore recent developments in that area will be discussed and their suitability for future parallel quantum chemistry computing demands considered.

DOI
10.4018/978-1-61350-116-0.ch011

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Quantum chemistry was a compute intensive field from the beginning. It was also an early adopter of parallel computing, and hence, has more than twenty years of experience with parallelism. However, recently parallel computing has seen dramatic changes, such as the rise of multi-core architectures, hybrid computing, and the prospect of exa-scale machines requiring 1 billion concurrent threads. It is doubtful that current approaches can address the challenges ahead. As a result, the field finds itself at a crossroads, facing the challenge to successfully identify the way forward. This chapter tells a story in two parts. First, the achievements to date are considered, offering insights learned so far. Second, we look at paradigms based on directed acyclic graphs (DAG). The computer science community is strongly advocating this paradigm, but the quantum chemistry community has no experience with this approach. Therefore recent developments in that area will be discussed and their suitability for future parallel quantum chemistry computing demands considered.
Quantum chemistry was a compute intensive field from the beginning. It was also an early adopter of parallel computing, and hence, has more than twenty years of experience with parallelism. However, recently parallel computing has seen dramatic changes, such as the rise of multi-core architectures, hybrid computing, and the prospect of exa-scale machines requiring 1 billion concurrent threads. It is doubtful that current approaches can address the challenges ahead. As a result, the field finds itself at a crossroads, facing the challenge to successfully identify the way forward. This chapter tells a story in two parts. First, the achievements to date are considered, offering insights learned so far. Second, we look at paradigms based on directed acyclic graphs (DAG). The computer science community is strongly advocating this paradigm, but the quantum chemistry community has no experience with this approach. Therefore recent developments in that area will be discussed and their suitability for future parallel quantum chemistry computing demands considered.
The topic of this dissertation stands at the crossroads of two emerging technologies: highly-parallel computing and quantum computing. Both seek to go beyond the limits of current computing practice: in the absence of further miniaturization, highly-parallel computing seeks to increase the available computer power substantially. Quantum Computing looks beyond the classical approach to computation, leveraging quantum-mechanical effects to increase computing power in a fundamental way. In the absence and also in the presence of actual quantum computers it is crucial to support the development of quantum computer applications by creating an appropriate software framework. Creating such a framework currently faces two major research challenges. First, one needs to separate the realization of the quantum computer from the software running on it, whether it is an actual physical or a simulated realization. Our first contribution lies with defining a layered software architecture, ranging from a design tool at the higher abstraction levels to a simulated execution layer underneath. At its core sits a ‘Quantum Virtual Machine’ using the Measurement Calculus as the underlying quantum computational model. Such a virtual machine layer makes the application developed on top of it oblivious to how it is executed. The simulated execution of this quantum virtual machine is fundamentally a computationally intensive task. Parallel computing offers a way to throw more computational resources at the problem, in the light of the saturation of sequential performance of current computers. Simulation is the domain of the second challenge, as it has very high performance requirements. The simulation problem thus becomes: how does one expose the inherent parallelism of quantum computing simulation in a fundamental way, so as to be usable for the highly-parallel computers to come? Our second contribution is to provide a formal translation of quantum programs to highly-parallel dataflow computations, and to develop a virtual environment that compiles and executes such programs. This work demonstrates several properties of both the formal as well as the virtual level of computation that validate our approach.
We first give a brief, incomplete overview over historic milestones leading to the emergence of quantum chemistry, starting from John Dalton's earliest attempts to describe the atom in the early 19th century. After the formulation of the Schrödinger equation in 1926 and the first successful description of covalent bonding using the new theory, it became soon clear that the main challenge ahead was to find efficient approximations to the Schrödinger equation, as was famously stated by Paul A. M. Dirac in 1929. Since then, many quantum-chemical approximations have been introduced, with a key problem being the exponential increase of the computational cost with the system size when approaching the exact solution of the Schrödinger equation. In the second part, we will hence focus on selected techniques to overcome the scaling problem. Finally, we close with some insights into the new and challenging field of reaction network exploration, offering a glimpse into potential future directions of quantum chemistry.
High performance computing (HPC) is renowned for its capacity to tackle complex problems. Meanwhile, quantum computing (QC) provides a potential way to accurately and efficiently solve quantum chemistry problems. The emerging field of quantum-centric high performance computing (QCHPC), which merges these two powerful technologies, is anticipated to enhance computational capabilities for solving challenging problems in quantum chemistry. The implementation of QCHPC for quantum chemistry requires interdisciplinary research and collaboration across multiple fields, including quantum chemistry, quantum physics, computer science and so on. This perspective provides an introduction to the quantum algorithms that are suitable for deployment in QCHPC, focusing on conceptual insights rather than technical details. Parallel strategies to implement these algorithms on quantum-centric supercomputers are discussed. We also summarize high performance quantum emulating simulators, which are considered a viable tool to explore QCHPC. We conclude with challenges and outlooks in this field.
For many years it has been recognized that fundamental physical constraints such as the speed of light will limit the ultimate speed of single processor computers to less than about three billion floating point operations per second (3 GFLOPS). This limitation is becoming increasingly restrictive as commercially available machines are now within an order of magnitude of this asymptotic limit. A natural way to avoid this limit is to harness together many processors to work on a single computational problem. In principle, these parallel processing computers have speeds limited only by the number of processors one chooses to acquire. The usefulness of potentially unlimited processing speed to a computationally intensive field such as quantum chemistry is obvious. If these methods are to be applied to significantly larger chemical systems, parallel schemes will have to be employed. For this reason we have developed distributed-memory algorithms for a number of standard quantum chemical methods. We are currently implementing these on a 32 processor Intel hypercube. In this paper we present our algorithm and benchmark results for one of the bottleneck steps in quantum chemical calculations: the four index integral transformation.