Anton 3 is the newest member in a family of supercomputers specially designed for atomic-level simulation of molecules relevant to biology (e.g., DNA, proteins, and drug molecules). Anton 3 achieves order-of-magnitude improvements in time-to-solution over its predecessor, Anton 2 (the current state of the art), and is over 100-fold faster than any other currently available supercomputer, thereby enabling broad new avenues of research on critical questions in biology and drug discovery. This speedup means that a 512-node Anton 3 simulates a million atoms at over 100 microseconds per day. Furthermore, Anton 3 attains this performance while consuming an order of magnitude less energy per simulated microsecond than any other machine. Like its predecessors, Anton 3 was designed from the ground up around a new custom chip to best exploit the capabilities offered by new technologies. We present here the main architectural and algorithmic developments that were necessary to achieve such significant advances.

Anton 2 is a second-generation special-purpose supercomputer for molecular dynamics simulations that achieves significant gains in performance, programmability, and capacity compared to its predecessor, Anton 1. The architecture of Anton 2 is tailored for fine-grained event-driven operation, which improves performance by increasing the overlap of computation with communication, and also allows a wider range of algorithms to run efficiently, enabling many new software-based optimizations. A 512-node Anton 2 machine, currently in operation, is up to ten times faster than Anton 1 with the same number of nodes, greatly expanding the reach of all-atom bio molecular simulations. Anton 2 is the first platform to achieve simulation rates of multiple microseconds of physical time per day for systems with millions of atoms. Demonstrating strong scaling, the machine simulates a standard 23,558-atom benchmark system at a rate of 85 μs/day -- 180 times faster than any commodity hardware platform or general-purpose supercomputer.

Exploiting parallelism to accelerate a computation typically involves dividing it into many small tasks that can be assigned to different processing elements. An efficient execution schedule for these tasks can be difficult or impossible to determine in advance, however, if there is uncertainty as to when each task's input data will be available. Ideally, each task would run in direct response to the arrival of its input data, thus allowing the computation to proceed in a fine-grained event-driven manner. Realizing this ideal is difficult in practice, and typically requires sacrificing flexibility for performance.

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.

Anton is a recently completed special-purpose supercomputer designed for molecular dynamics (MD) simulations of biomolecular systems. The machine's specialized hardware dramatically increases the speed of MD calculations, making possible for the first time the simulation of biological molecules at an atomic level of detail for periods on the order of a millisecond---about two orders of magnitude beyond the previous state of the art. Anton is now running simulations on a timescale at which many critically important, but poorly understood phenomena are known to occur, allowing the observation of aspects of protein dynamics that were previously inaccessible to both computational and experimental study. Here, we report Anton's performance when executing actual MD simulations whose accuracy has been validated against both existing MD software and experimental observations. We also discuss the manner in which novel algorithms have been coordinated with Anton's co-designed, application-specific hardware to achieve these results.

Anton is a recently completed special-purpose supercomputer designed for molecular dynamics (MD) simulations of biomolecular systems. The machine's specialized hardware dramatically increases the speed of MD calculations, making possible for the first time the simulation of biological molecules at an atomic level of detail for periods on the order of a millisecond---about two orders of magnitude beyond the previous state of the art. Anton is now running simulations on a timescale at which many critically important, but poorly understood phenomena are known to occur, allowing the observation of aspects of protein dynamics that were previously inaccessible to both computational and experimental study. Here, we report Anton's performance when executing actual MD simulations whose accuracy has been validated against both existing MD software and experimental observations. We also discuss the manner in which novel algorithms have been coordinated with Anton's co-designed, application-specific hardware to achieve these results.

One of the major design verification challenges in the development of Anton, a massively parallel special-purpose machine for molecular dynamics, was to provide evidence that computations spanning more than a quadrillion clock cycles will produce valid scientific results. Our verification methodology addressed this problem by using a hierarchy of RTL, architectural, and numerical simulations. Block- and chip-level RTL models were verified by means of extensive co-simulation with a detailed C++ architectural simulator, ensuring that the RTL models could perform the same molecular dynamics computations as the architectural simulator. The output of the architectural simulator was compared to a parallelized numerical simulator that produces bitwise identical results to Anton, and is fast enough to verify the long-term numerical stability of computations on Anton. These explicit couplings between adjacent levels of the simulation hierarchy created a continuous verification chain from molecular dynamics to individual logic gates.

The ability to perform long, accurate molecular dynamics (MD) simulations involving proteins and other biological macro-molecules could in principle provide answers to some of the most important currently outstanding questions in the fields of biology, chemistry and medicine. A wide range of biologically interesting phenomena, however, occur over time scales on the order of a millisecond--about three orders of magnitude beyond the duration of the longest current MD simulations.

A structured video consists of a collection of background objects, characters, spatial and temporal constructs, and rendering features. Assuming a platform consisting of a fixed amount of memory and a magnetic disk drive, this study presents a resource scheduler for the continuous display of structured video that minimizes both the latency observed by a display and its required amount of memory.

The past decade has witnessed a proliferation of respositories whose workload consists of queries that retrieve information. These repositories provide on-line access to vast amount of data and serve as an integral component of many application domains (e.g., library information systems, scientific applications, entertainment industry). Their storage subsystem is expected to be hierarchical consisting of memory, disk drives, and one or more tertiary storage devices. The database resides permanently on the tertiary storage devices and objects are swapped onto the magnetic disk drives on demand (and deleted once the disk storage capacity is exhausted). This may fragment the disk space over a period of time, resulting in a non-contiguous layout of an object across the surface of a disk drive. This is undesirable because, once the object is referenced, the disk drive is required to reposition its read head multiple times (incur seek operations) when retrieving the object, resulting in a low performance.

Article 2d-bubblesorting in average time O(√N lg N)* Share on Author: Doug Ierardi Univ. of Southern California, Los Angeles Univ. of Southern California, Los AngelesView Profile Authors Info & Claims SPAA '94: Proceedings of the sixth annual ACM symposium on Parallel algorithms and architecturesAugust 1994 Pages 36–45https://doi.org/10.1145/181014.181025Online:01 August 1994Publication History 4citation14DownloadsMetricsTotal Citations4Total Downloads14Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Previous results for finding optimal motions for a rod/ladder concentrated on the path planning problems. In this paper, we focus on the optimal trajectory planning problems and provide an explicit solution to find the shortest time trajectory for an ideal rod in the plane with velocity constraints.

We consider the problem of counting the number of points on a plane curve, given by a homogeneous polynomial F/spl isin/F/sub p/[x, y, z], which is rational over the ground field F/sub p/. More precisely, we show that if we are given a projective plane curve C of degree n, and if C has only ordinary multiple points, then one can compute the number of F/sub p/-rational points on C in randomized time (log p)/sup /spl Delta// where /spl Delta/=(degF)/sup O(1/). The complexity of this construction improves previously known bounds for this problem by at least an order of magnitude.>

Several computational problems concerning the construction of rational functions and intersecting curves over a given curve are studied. The first problem is to construct a rational function with prescribed zeros and poles over a given curve. More precisely, let C be a smooth projective curve and assume as given an affine plane model F(x,y)=0 for C, a finite set of points P/sub i/=(X/sub i/, Y/sub i/) with F (X/sub i/, Y/sub i/)=0 and natural numbers n/sub i/, and a finite set of points Q/sub i/=(X/sub j/, Y/sub j/) with F(X/sub j/, Y/sub j/)=0 and natural numbers m/sub j/. The problem is to decide whether there is a rational function which has zeros at each point P/sub i/ of order n/sub i/, poles at each Q/sub j/ of order m/sub j/, and no zeros or poles anywhere else on C. One would also like to construct such a rational function if one exists. An efficient algorithm for solving this problem when the given plane curve has only ordinary multiple points is given.>

In this paper we develop a fast parallel procedure for deciding when a set of multivariate polynomials with coefficients in an arbitrary field K have a common algebraic solution. Moreover, since the proposed algorithm is algebraic, it easily yields a procedure for quantifier elimination in the theory of an arbitrary algebraically closed field.