State recoverability is a crucial aspect of speculative Time Warp-based Parallel Discrete Event Simulation. In the literature, we can identify three major classes of techniques to support the correct restoration of a previous simulation state upon the execution of a rollback operation: state checkpointing/restore, manual reverse computation and automatic reverse computation. The latter class has been recently supported by relying either on binary code instrumentation or on source-to-source code transformation. Nevertheless, both solutions are not intrinsically meant to support a reversible execution of third-party shared libraries, which can be pretty useful when implementing complex simulation models.

Discrete event simulations (DES) are central to exploration of "what-if" scenarios in many domains including networks, storage devices, and chip design. Accurate simulation of dynamically varying behavior of large components in these domains requires the DES engines to be scalable and adaptive in order to complete simulations in a reasonable time. This paper takes a step towards development of such a simulation engine by redesigning ROSS, a parallel DES engine in MPI, in Charm++, a parallel programming framework based on the concept of message-driven migratable objects managed by an adaptive runtime system. In this paper, we first show that the programming model of Charm++ is highly suitable for implementing a PDES engine such as ROSS. Next, the design and implementation of the Charm++ version of ROSS is described and its benefits are discussed. Finally, we demonstrate the performance benefits of the Charm++ version of ROSS over its MPI counterpart on IBM's Blue Gene/Q supercomputers. We obtain up to 40% higher event rate for the PHOLD benchmark on two million processes, and improve the strong-scaling of the dragonfly network model to 524, 288 processes with up to 5x speed up at lower process counts.

We have created a scalable implementation of the kinetic Monte-Carlo method, SPOCK (Scalable Parallel Optimistic Crystal Kinetics). Unlike most reported parallel implementations relying on approximation to achieve parallelism, our parallelization is exact and accomplished using the Time Warp paradigm. We demonstrate that our implementation exhibits near perfect scaling for two different and important classes of systems. It runs efficiently on Vulcan, a 24 thousand node BlueGene/Q machine, using all ~400 thousand cores and ~1.6 million MPI tasks. Further, we have run production simulations using the full Vulcan machine and requiring nearly all available system memory. In this paper we demonstrate these results, and discuss some important implementation details. The kinetic Monte-Carlo method is ubiquitous within the natural sciences, and important classes of problems have so far been limited to sequential simulation. For many scientific simulations, an exact parallel implementation of the kinetic Monte-Carlo method has the potential of being game changing.

The fully automatic generation of code that establishes the reversibility of arbitrary C/C++ code has been a target of research and engineering for more than a decade as reverse computation has become a central notion in large scale parallel discrete event simulation (PDES). The simulation models that are implemented for PDES are of increasing complexity and size and require various language features to support abstraction, encapsulation, and composition when building a simulation model. In this paper we focus on parallel simulation models that are written in C++ and present an approach and an evaluation for a fully automatically generated reversible code for a kinetic Monte-Carlo application implemented in C++. Although a significant runtime overhead is introduced with our technique, the assurance that the reverse code is generated automatically and correctly, is an enormous win that allows simulation model developers to write forward event code using the entire C++ language, and have that code automatically transformed into reversible code to enable parallel execution with the Rensselaer's Optimistic Simulation System (ROSS).

Time Warp is an optimistic synchronization protocol for parallel discrete event simulation that coordinates the available parallelism through its rollback and antimessage mechanisms. In this paper we present the results of a strong scaling study of the ROSS simulator running Time Warp with reverse computation and executing the well-known PHOLD benchmark on Lawrence Livermore National Laboratory's Sequoia Blue Gene/Q supercomputer. The benchmark has 251 million PHOLD logical processes and was executed in several configurations up to a peak of 7.86 million MPI tasks running on 1,966,080 cores. At the largest scale it processed 33 trillion events in 65 seconds, yielding a sustained speed of 504 billion events/second using 120 racks of Sequoia. This is by far the highest event rate reported by any parallel discrete event simulation to date, whether running PHOLD or any other benchmark. Additionally, we believe it is likely to be the largest number of MPI tasks ever used in any computation of any kind to date.

Abstract This paper analyzes the performance of a discrete‐event combat simulation executed on a parallel processor under control of the Time Warp Operating System. Time Warp is in a class of distributed simulation methods called Optimistic methods which have proven to be useful over a wide range of simulations. The combat simulation used for this performance study, called STB88, is a division‐corps model incorporating a number of different types of computations. The speed‐up for three versions of this model on the Caltech/JPL Mark III Hypercube and the BBN Butterfly parallel processors was measured relative to an efficient sequential execution of the same model on the same hardware. The results indicate that STB88 version 1 achieves a speed‐up of 28.6 on 60 Mark III processors, while STB88 version 2 achieves a speed‐up of 36.8 on 100 Butterfly processors. Version 3 of STB88 achieved a speed‐up of 38.5 on 128 Mark III processors. The versions differed only in their interface to Time Warp. On the Butterfly, the sequential execution completed in 2 hours, while the 100 processor execution completed in 3.2 minutes.

This paper describes the Time Warp Operating System, under development for three years at the Jet Propulsion Laboratory for the Caltech Mark III Hypercube multi-processor. Its primary goal is concurrent execution of large, irregular discrete event simulations at maximum speed. It also supports any other distributed applications that are synchronized by virtual time.

In this paper we introduce the Time Warp mechanism as a new method for concurrency control in distributed database systems. Its major distinguishing features are first, a unification of transactions and data as two forms of the more general notion of object, and second, the use of object rollback as the fundamental tool for synchronization instead of blocking or abortion.

Virtual time is a new paradigm for organizing and synchronizing distributed systems which can be applied to such problems as distributed discrete event simulation and distributed database concurrency control. Virtual time provides a flexible abstraction of real time in much the same way that virtual memory provides an abstraction of real memory. It is implemented using the Time Warp mechanism, a synchronization protocol distinguished by its reliance on lookahead-rollback, and by its implementation of rollback via antimessages.