Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

An integrated forecasting and data assimilation system has been and is continuing to be developed by the Meteorological Research Branch (MRB) in partnership with the Canadian Meteorological Centre (CMC) of Environment Canada. Part I of this two-part paper motivates the development of the new system, summarizes various considerations taken into its design, and describes its main characteristics.

https://journals.ametsoc.org/doi/pdf/10.1175/1520-0493%281998%29126%3C1373%3ATOCMGE%3E2.0.CO%3B2

The Operational CMC–MRB Global Environmental Multiscale (GEM) Model. Part I: Design Considerations and Formulation

Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials

Optics offers unique opportunities for reducing energy in information processing and communications while simultaneously resolving the problem of interconnect bandwidth density inside machines. Such energy dissipation overall is now at environmentally significant levels; the source of that dissipation is progressively shifting from logic operations to interconnect energies. Without the prospect of substantial reduction in energy per bit communicated, we cannot continue the exponential growth of our use of information. The physics of optics and optoelectronics fundamentally addresses both interconnect energy and bandwidth density, and optics may be the only scalable solution to such problems. Here we summarize the corresponding background, status, opportunities, and research directions for optoelectronic technology and novel optics, including subfemtojoule devices in waveguide and novel two-dimensional (2-D) array optical systems. We compare different approaches to low-energy optoelectronic output devices and their scaling, including lasers, modulators and LEDs, optical confinement approaches (such as resonators) to enhance effects, and the benefits of different material choices, including 2-D materials and other quantum-confined structures. With such optoelectronic energy reductions, and the elimination of line charging dissipation by the use optical connections, the next major interconnect dissipations are in the electronic circuits for receiver amplifiers, timing recovery, and multiplexing. We show we can address these through the integration of photodetectors to reduce or eliminate receiver circuit energies, free-space optics to eliminate the need for timing and multiplexing circuits (while also solving bandwidth density problems), and using optics generally to save power by running large synchronous systems. One target concept is interconnects from ∼1 cm to ∼10 m that have the same energy (∼10 fJ/bit) and simplicity as local electrical wires on chip.

Attojoule Optoelectronics for Low-Energy Information Processing and Communications

Modern data centers increasingly rely on interconnects for delivering critical communications connectivity among numerous servers, memory, and computation resources. Data center interconnects turned to optical communications almost a decade ago, and the recent acceleration in data center requirements is expected to further drive photonic interconnect technologies deeper into the systems architecture. This review paper analyzes optical technologies that will enable next-generation data center optical interconnects. Recent progress addressing the challenges of terabit/s links and networks at the laser, modulator, photodiode, and switch levels is reported and summarized.

Recent advances in optical technologies for data centers: a review

Triangle counting serves as a key building block for a set of important graph algorithms in network science. In this paper, we address the IEEE HPEC Static Graph Challenge problem of triangle counting, focusing on obtaining the best parallel performance on a single multicore node. Our implementation uses a linear algebra-based approach to triangle counting that has grown out of work related to our miniTri data analytics miniapplication [1] and our efforts to pose graph algorithms in the language of linear algebra. We leverage KokkosKernels to implement this approach efficiently on multicore architectures. Our performance results are competitive with the fastest known graph traversal-based approaches and are significantly faster than the Graph Challenge reference implementations, up to 670,000 times faster than the C++ reference and 10,000 times faster than the Python reference on a single Intel Haswell node.

Fast linear algebra-based triangle counting with KokkosKernels

Face-to-face comparison of major large scale HPC interconnects.Review of challenges and solutions for increased use of optics in HPC.Description of optical switching, in terms of principle, limitations, technical requirements and benefits. Large-scale high performance computing is permeating nearly every corner of modern applications spanning from scientific research and business operations, to medical diagnostics, and national security. All these communities rely on computer systems to process vast volumes of data quickly and efficiently, yet progress toward increased computing power has experienced a slowdown in the last number of years. The sheer cost and scale, stemming from the need for extreme parallelism, are among the reasons behind this stall. In particular, very large-scale, ultra-high bandwidth interconnects, essential for maintaining computation performance, represent an increasing portion of the total cost budget.Photonic systems are often cited as ways to break through the energy-bandwidth limitations of conventional electrical wires toward drastically improving interconnect performance. This paper presents an overview of the challenges associated with large-scale interconnects, and reviews how photonic technologies can contribute to addressing these challenges. We review some important aspects of photonics that should not be underestimated in order to truly reap the benefits of cost and power reduction.

Optical interconnects for extreme scale computing systems

memkind: An Extensible Heap Memory Manager for Heterogeneous Memory Platforms and Mixed Memory Policies.

An investigation of the performance portability of OpenCL

To achieve exascale computing, fundamental hardware architectures must change. This will significantly impact scientific applications that run on current high performance computing (HPC) systems, many of which codify years of scientific domain knowledge and refinements for contemporary computer systems. To adapt to exascale architectures, developers must be able to reason about new hardware and determine what programming models and algorithms will provide the best blend of performance and energy efficiency in the future. An abstract machine model is designed to expose to the application developers and system software only the aspects of the machine that are important or relevant to performance and code structure. These models are intended as communication aids between application developers and hardware architects during the co-design process. A proxy architecture is a parameterized version of an abstract machine model, with parameters added to elucidate potential speeds and capacities of key hardware components. These more detailed architectural models enable discussion among the developers of analytic models and simulators and computer hardware architects and they allow for application performance analysis, system software development, and hardware optimization opportunities. In this paper, we present a set of abstract machine models and show how they might be used to help software developers prepare for exascale. We then apply parameters to one of these models to demonstrate how a proxy architecture can enable a more concrete exploration of how well application codes map onto future architectures.

/pdf/abstract-machine-models-and-proxy-architectures-for-exascale-2w5jyca26m.pdf

Simon D. Hammond

Papers

Fast linear algebra-based triangle counting with KokkosKernels

Optical interconnects for extreme scale computing systems

memkind: An Extensible Heap Memory Manager for Heterogeneous Memory Platforms and Mixed Memory Policies.

An investigation of the performance portability of OpenCL

Abstract machine models and proxy architectures for exascale computing