monte battaglia.
Monte Carlo PCA 2.0.3
Monte Carlo PCA - Computes parallel analysis for factor analysis more>>
The user can specify 3-200 variables, 50-2500 subjects, and 1-1000 replications. 1.4MB self-extracting file.
Source : RealBasic.
Generate Monte Carlo Simulation 1.0
Generate Monte Carlo Simulation - AppleScript for OmniGraffle more>>
Medal of Honor: Breakthrough 2.40b
Race to capture Messina in Sicily, and finally repel the German army at the historic battle of Monte Battaglia. more>>
An expansion pack for Medal of Honor: Allied Assault. As U.S. Army Sergeant John Baker, battle at Kasserine Pass in North Africa, race to capture Messina in Sicily, and finally repel the German army at the historic battle of Monte Battaglia in the heart of Italy. Armed with a new arsenal of weapons, players will fight alone and with squad mates as they rush to stop the Axis powers. Medal of Honor Allied Assault Breakthrough, the third expansion pack, presents new, more challenging situations and provides more options, with over 25 new soldiers, including Italian, American, German, and British troops, and 12 new Allied and Axis weapons, including authentic Italian weapons.
Game Demo 1.0
Download from MacGameFiles (199MB)
View QuickTime Trailer
Game Feauture: The Many Faces of War: Medal of Honor: Breakthrough, Call of Duty, and Delta Force: Black Hawk Down
Related Resources:
- Feature: We Want You ? To Play Medal of Honor: Allied Assault
- Feature: First-Person History: WWII Comes Alive in Medal of Honor: Allied Assault Spearhead
- Medal of Honor Dedicated Server: Allows you to run a server under for multiplayer games.
- GSC Breakthrough: Host your own games.
Monte Carlo PCA for Parallel Analysis 2.0.3
Monte Carlo PCA for Parallel Analysis is a powerful and splendid tool which computes Parallel Analysis criteria (eigenvalues) for determining the number of factors to retain for rotation in factor analysis. more>> Monte Carlo PCA for Parallel Analysis 2.0.3 is a powerful and splendid tool which computes Parallel Analysis criteria (eigenvalues) for determining the number of factors to retain for rotation in factor analysis.
Requirements: Mac OS X 10.2 or later.
Devil's Brigade Lux 1
Devils Brigade Lux is designed to be an addictive tactical game in which you can take command of the elite Devils Brigade unit from WWII. more>>
Devil's Brigade Lux 1 is designed to be an addictive tactical game in which you can take command of the elite Devils Brigade unit from WWII. Based on the Lux game engine. Mission info: upon arrival at the Camino massif, the two targets selected for the FSSF were Monte la Difensa and Monte la Remetanea. The first, Difensa, appeared to be virtually impregnable. The dormant volcano provided the German troops with an ideal defensive position.
Armed with machine guns and mortars, the German defenders could easily hold off an assault on the ramp-like southern slope which offered the only passable approach to the top. The northern face of the mountain featured almost vertical 200-foot high cliffs. Two previous attempts to take Difensa had been made on the southern slope by the 3rd and 36th US Infantry Divisions.
Elfin Clash 1.1
Elfin Clash - Fantasy fighting game more>>
Take on the role of the High Elf and defeat the Dark Elf with your skills as a swordswoman.
Note: You should switch your monitor to 256 colours before playing.
MacParallel Analysis 1.0
MacParallel Analysis contains tables of eigenvalues produced by Monte Carlo simulations more>>
Risk Engine 1.2.4
Run detailed Monte Carlo analyses on your iWork Numbers models. more>>
Risk Engine 1.2.4 is a useful utility that works with both iWork Numbers '09 and Microsoft Excel 2008 giving users of the most advanced spreadsheet software access to modern analysis methods.
Major Features:
- Analysis options include the ability for user defined seeds to enable accurate re-creation or previous analyses.
- Automatically calculates key statistical parameters for analyses including mean, standard deviation and percentiles.
- Built in generation of histograms for analyses including user defined formatting and drag and drop support.
- Built in probability distributions for inputs to the model including normal, uniform, triangular and lognormal distributions. The user can also define any probability distribution using the spreadsheet interface.
Enhancements:
- Added the Binomial distribution to the built in distributions.
- Automated calculation of statistics for user defined distributions.
- Improved handling of non-number cell formats for iWork Numbers.
- Added the ability to launch Numbers and Excel from within Risk Engine.
- Improved management of values entered in the Model Data window to avoid analyses using old values being run.
- Improved checks for systems where iWork or Excel are not installed to avoid application crashes.
Requirements: A copy of Numbers from iWork '09 or Microsoft Office 2008.
<<lessRisk Engine 1.2.2 is an efficient tool which can run detailed Monte Carlo analyses on your iWork ... iWork Numbers models to allow you to run detailed Monte Carlo analyses with multiple inputs andLicense:Freeware
Dragon Grabber 1.2
Dragon Grabber - Cute medieval arcade game more>>
Grab your trusty net and capture all the evil dragons. But be careful not to capture the good dragons.
Note: You should switch your monitor to 256 colours before playing.
Electronics Optimizer 1.0.4
Electronics Optimizer is an electronic circuit analysis tool more>>
Electronics Optimizer will help you quickly design a circuit (choosen from a standard library of circuits) to match your specified design parameters without drawing a schematic.
Moreover Electronics Optimizer has a calculator for first-cut estimates for designers.
CUDA 2.2
A C language development environment for CUDA-enabled GPUs. more>> A C language development environment for CUDA-enabled GPUs.
The CUDA Toolkit is a C language development environment for CUDA-enabled GPUs.
In a matter of a few years, the programmable graphics processor unit has
developed into an absolute computing workhorse. With multiple cores driven by very high memory bandwidth, todays GPUs offer incredible resources for both graphics and non-graphics processing.
The main reason behind such an evolution is that the GPU is specialized for compute-intensive, highly parallel computation exactly what graphics rendering is about and therefore is designed such that more transistors are devoted to data processing rather than data caching and flow control.
The CUDA development environment includes:
- nvcc C compiler
- CUDA FFT and BLAS libraries for the GPU
- Profiler
- gdb debugger for the GPU (alpha available in March, 2008)
- CUDA runtime driver (now also available in the standard NVIDIA GPU driver)
- CUDA programming manual
The CUDA Developer SDK provides examples with source code to help you get started with CUDA. Examples include:
- Parallel bitonic sort
- Matrix multiplication
- Matrix transpose
- Performance profiling using timers
- Parallel prefix sum (scan) of large arrays
- Image convolution
- 1D DWT using Haar wavelet
- OpenGL and Direct3D graphics interoperation examples
- CUDA BLAS and FFT library usage examples
- CPU-GPU C- and C++-code integration
- Binomial Option Pricing
- Black-Scholes Option Pricing
- Monte-Carlo Option Pricing
- Parallel Mersenne Twister (random number generation)
- Parallel Histogram
- Image Denoising
- Sobel Edge Detection Filter
- MathWorks MATLAB Plug-in
Main features:
- Standard C programming language enabled on a GPU.
- Unified hardware and software solution for parallel computing on CUDA-enabled NVIDIA GPUs.
- CUDA compatible GPUs range from lower power notebook GPUs to high performance, multi-GPU systems.
- CUDA-enabled GPUs support the Parallel Data Cache and Thread Execution Manager.
- Standard numerical libraries for FFT (Fast Fourier Transform) and BLAS (Basic Linear Algebra Subroutines).
- Dedicated CUDA driver for computing.
- Optimized direct upload and download path from the CPU to CUDA-enabled GPU.
- CUDA driver interoperates with OpenGL and DirectX graphics drivers.
- Support for Linux 32/64-bit and Windows XP 32/64-bit operating systems.
- Direct driver and assembly level access through CUDA for research and language development.
Enhancements
- Visual Profiler for the GPU - The most common step in tuning application performance is profiling the application and then modifying the code. The CUDA Visual Profiler is a graphical tool that enables the profiling of C applications running on the GPU. This latest release of the CUDA Visual Profiler includes metrics for memory transactions, giving developers visibility into one of the most important areas they can tune to get better performance.
- Improved OpenGL Interop - Delivers improved performance for Medical Imaging and other OpenGL applications running on Quadro GPUs when computing with CUDA and rendering OpenGL graphics functions are performed on different GPUs.
- Texture from Pitch Linear Memory - Delivers up to 2x bandwidth savings for video processing applications.
- Zero-copy - Enables streaming media, video transcoding, image processing and signal processing applications to realize significant performance improvements by allowing CUDA functions to read and write directly from pinned system memory. This reduces the frequency and amount of data copied back and forth between GPU and CPU memory. Supported on MCP7x and GT200 and later GPUs.
- Pinned Shared Sysmem - Enables applications that use multiple GPUs to achieve better performance and use less total system memory by allowing multiple GPUs to access the same data in system memory. Typical multi-GPU systems include Tesla servers, Tesla Personal Supercomputers, workstations using QuadroPlex deskside units and consumer systems with multiple GPUs.
- Asynchronous memcopy on Vista - Allows applications to realize significant performance improvements by copying memory asynchronously. This feature was already available on other supported platforms but is now available on Vista.
- Hardware Debugger for the GPU - Developers can now use a hardware level debugger on CUDA-enabled GPUs that offers the simplicity of the popular open-source GDB debugger yet enables a developer to easily debug a program that is running 1000s of threads on the GPU. This CUDA GDB debugger for Linux has all the features required to debug directly on the GPU, including the ability to set breakpoints, watch variables, inspect state, etc.
- Exclusive Device Mode - This system configuration option allows an application to get exclusive use of a GPU, guaranteeing that 100% of the processing power and memory of the GPU will be dedicated to that application. Multiple applications can still be run concurrently on the system, but only one application can make use of each GPU at a time. This configuration is particularly useful on Tesla cluster systems where large applications may require dedicated use of one or more GPUs on each node of a Linux cluster.
Pinned Memory Support:
- These new memory management functions (cuMemHostAlloc() and cudaHostAlloc()) enable pinned memory to be made "portable" (available to all CUDA contexts), "mapped" (mapped into the CUDA address space), and/or "write combined" (not cached and faster for the GPU to access).
- cuMemHostAlloc
- cuMemHostGetDevicePointer
- cudaHostAlloc
- cudaHostGetDevicePointer
Function attribute query:
- This function allows applications to query various function properties.
- cuFuncGetAttribute
2D Texture reads from pitch linear memory:
- You can bind linear memory that you get from cuMemAlloc() or cudaMalloc() directly to a 2D texture. In previous releases, you were only able to bind cuArrayCreate() or cudaMallocArray() arrays to 2D textures.
- cuTexRefSetAddress2D
- cudaBindTexture2D
Flags for event creation:
- Applications can now create events that use blocking synchronization.
- cudaEventCreateWithFlags
New device management and context creation flags:
- The function cudaSetDeviceFlags() allows the application to specify attributes such as mapping host memory and support for blocking synchronization.
- cudaSetDeviceFlags
Improved runtime device management:
- The runtime now defaults to attempting context creation on other devices in the system before returning any failure messages. The new call cudaSetValidDevices() allows the application to specify a list of acceptable devices for use.
- cudaSetValidDevices
Driver/runtime version query functions:
- Applications can now directly query version information about the underlying driver/runtime.
- cuDriverGetVersion
- cudaDriverGetVersion
- cudaRuntimeGetVersion
New device attribute queries:
- CU_DEVICE_ATTRIBUTE_INTEGRATED
- CU_DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY
- CU_DEVICE_ATTRIBUTE_COMPUTE_MODE
Documentation:
- Doxygen-generated and cross-referenced html, pdf, and man pages.
- Runtime API
- Driver API
Crux 1.0.0
A free software toolkit for molecular phylogenetic inference that runs on (at least) Mac OS X, Linux, FreeBSD more>> A free software toolkit for molecular phylogenetic inference that runs on (at least) Mac OS X, Linux, FreeBSD
Crux is structured as a set of Python modules, which makes it possible to quickly develop Python scripts that perform unique, non-canned analyses.
The Python language is elegant and powerful, but it does not have any high performance implementations, which does make it a challenge to use for scientific computing.
Crux is actually implemented as a combination of Python, Cython and C code. Cython is a superset of Python, with various features that allow compiled results to substantially out-perform pure Python code.
The result is that Crux supports a wide variety of users:
- End users can use canned scripts, either those included with Crux, or scripts developed for them by computer-savvy collaborators.
- Computer-savvy users can develop custom scripts that utilize the components of Crux in unforseen ways.
- Power users can use Crux as a component of a custom software package, or extend Crux itself. The source code is available under a very permissive license, with the intent that it be used.
Main features:
- Multifurcating trees can be manipulated at a low level. Various standard operations are implemented, such as canonization (ordering the tree according to a ranking of the taxa), collapsing of zero-length branches, Robinson-Foulds distance computation, and so on.
- Parsers are included for various file formats, including Newick trees, FASTA sequences, and PHYLIP distance matrices.
- Pairwise distances between sequences can be computed based on percent identity, or using methods that correct for multiple hits (Jukes-Cantor, Kimura, and logDet).
- The neighbor joining (NJ) and relaxed neighbor joining (RNJ) implementations are among the fastest in existence, along with Clearcut.
- Tree log-likelihoods can be computed under a variety of models, including all specializations of GTR+I+? and mixture models. Tree likelihoods can be computed in parallel via pthreads. Additionally, Crux is capable of simulating character data under any model its likelihood engine is capable of. The huge range of simulation options makes a canned command line interface like that of Seq-Gen rather unweildy, so simulation is perhaps the most obvious compelling reason to develop Crux scripts.
- Bayesian Markov chain Monte Carlo (MCMC) methods (with Metropolis coupling and optional MPI support for parallel computation) can sample among non-nested models using reversible model jumps. Polytomous trees can be sampled, also via reversible jumps. In fact, every model parameter that Cruxs MCMC implementation estimates can be expunged via reversible jumps.
System requirements:
- Xcode (to build Crux from source)
Boomer 3.1.9
Boomer - Non-linear regression app for pharmacokinetic data more>>
Parameter estimation can be by normal or Bayesian methods. A comprehensive list of weighting schemes is included. The model to be analyzed can be entered as a system of integrated equations or as a system of differential equations. Differential equations are solved by a classical fourth order Runge-Kutta method, a fourth order Runge-Kutta-Gill method, a four/five Runge-Kutta-Fehlberg method, an Adams predictor-corrector method, or Gears method for stiff equations. Simulation with random error in parameters or data are also possible. A batch run can be repeated many times to assist in Monte Carlo type studies.
Enhancements:
- Fixed a bug that caused a segmentation error with more than 30 adjustable parameters ;-)
- added form feed to output.
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