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A redesigned version of Liberty City was introduced in Grand Theft Auto III (set in 2001). This iteration is only loosely based on New York, and incorporates elements from other U.S. cities, such as Philadelphia, Detroit, Boston, Chicago, and Baltimore. The city encompassess three main islands, which are gradually unlocked as the game's storyline progresses: Portland (based on the industrial areas of Brooklyn and Queens, with additional elements from Manhattan and Long Island), Staunton Island (based mostly on Manhattan), and Shoreside Vale (loosely based on North Jersey, The Bronx, Staten Island, and Upstate New York). The islands are connected by road bridges and an underground tunnel system. A tunnel leading out of Liberty City can be found in Shoreside Vale, but it is impassable by the player. This particular version of Liberty City returned in the prequels Grand Theft Auto Advance (set in 2000) and Grand Theft Auto: Liberty City Stories (set in 1998), albeit with several changes to reflect the different time periods. The city was also mentioned in Grand Theft Auto: Vice City and Grand Theft Auto: San Andreas, and was the setting of a mission in the latter.
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The London 1969 and London 1961 expansion packs for Grand Theft Auto take place within a fictionalised version of London during the 1960s. As such, they are the only games in the series to be set outside of the United States. The portion of the city used in the games is based on Central London, although heavily condensed and mostly geographically inaccurate. It consists of two landmasses, separated by the River Thames and connected by several road bridges. A fictionalised version of Manchester is also featured in the games.
There has been some controversy over a drug dealing minigame along with comments that some Nintendo games are being aimed at children (despite the fact that the game was rated Mature). The drug dealing mini-game allows players to peddle six types of drugs around the city, but the profit the player makes depends on market conditions, which will be based on the area in which they deal, and the level of regular service this area receives from them.
Unlike its immediate predecessors Grand Theft Auto III and Vice City, which needed loading screens when the player moved between different districts of the city, San Andreas has no load times when the player is in transit. The only loading screens in the game are for cut-scenes and interiors. Other differences between San Andreas and its predecessors include the switch from single-player to multiplayer Rampage missions (albeit not in the PC and mobile versions), and the replacement of the "hidden packages" with spray paint tags, hidden camera shots, horseshoes, and oysters to discover.
The Stochastic Simulation Algorithm (SSA) developed by Gillespie provides a powerful mechanism for exploring the behavior of chemical systems with small species populations or with important noise contributions. Gene circuit simulations for systems biology commonly employ the SSA method, as do ecological applications. This algorithm tends to be computationally expensive, so researchers seek an efficient implementation of SSA. In this program package, the Accelerated Exact Stochastic Simulation Algorithm (AESS) contains optimized implementations of Gillespie's SSA that improve the performance of individual simulation runs or ensembles of simulations used for sweeping parameters or to provide statistically significant results. Program summaryProgram title: AESS Catalogue identifier: AEJW_v1_0 Program summary URL: _v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: University of Tennessee copyright agreement No. of lines in distributed program, including test data, etc.: 10 861 No. of bytes in distributed program, including test data, etc.: 394 631 Distribution format: tar.gz Programming language: C for processors, CUDA for NVIDIA GPUs Computer: Developed and tested on various x86 computers and NVIDIA C1060 Tesla and GTX 480 Fermi GPUs. The system targets x86 workstations, optionally with multicore processors or NVIDIA GPUs as accelerators. Operating system: Tested under Ubuntu Linux OS and CentOS 5.5 Linux OS Classification: 3, 16.12 Nature of problem: Simulation of chemical systems, particularly with low species populations, can be accurately performed using Gillespie's method of stochastic simulation. Numerous variations on the original stochastic simulation algorithm have been developed, including approaches that produce results with statistics that exactly match the chemical master equation (CME) as well as other approaches that approximate the CME. Solution
We define some of the programming and system-level challenges facing the application of quantum processing to high-performance computing. Alongside barriers to physical integration, prominent differences in the execution of quantum and conventional programs challenges the intersection of these computational models. Following a brief overview of the state of the art, we discuss recent advances in programming and execution models for hybrid quantum-classical computing. We discuss a novel quantum-accelerator framework that uses specialized kernels to offload select workloads while integrating with existing computing infrastructure. We elaborate on the role of the host operating system to manage these unique accelerator resources, themore prospects for deploying quantum modules, and the requirements placed on the language hierarchy connecting these different system components. We draw on recent advances in the modeling and simulation of quantum computing systems with the development of architectures for hybrid high-performance computing systems and the realization of software stacks for controlling quantum devices. Finally, we present simulation results that describe the expected system-level behavior of high-performance computing systems composed from compute nodes with quantum processing units. We describe performance for these hybrid systems in terms of time-to-solution, accuracy, and energy consumption, and we use simple application examples to estimate the performance advantage of quantum acceleration. less
We report on updates to the accelerator controls for the Neutralized Drift Compression Experiment II, a pulsed induction-type accelerator for heavy ions. The control infrastructure is built around a LabVIEW interface combined with an Apache Cassandra backend for data archiving. Recent upgrades added the storing and retrieving of device settings into the database, as well as ZeroMQ as a message broker that replaces LabVIEW's shared variables. Converting to ZeroMQ also allows easy access via other programming languages, such as Python.