Search Results for "gpu max performance"

Newton is a high-performance, GPU-accelerated physics simulation engine designed primarily for robotics research, machine learning, and advanced simulation workflows. Built on top of NVIDIA Warp, it leverages GPU parallelism to deliver scalable and efficient simulation environments that support rapid iteration and experimentation. The engine extends previous simulation frameworks by introducing differentiable physics capabilities, allowing it to integrate seamlessly with machine learning models and optimization pipelines. ...

NOTICE OF CONSOLIDATION & PARTNERSHIP PENDING As of April 2026, the 20 pipelines of the QCAUS/PDPBioGen suites are undergoing consolidation for high-scale institutional research. Core 'Ford 2026' algorithms remain the proprietary IP of the Ford Peace and Justice Foundation. Academic users at partner institutions are currently performing validation; all other commercial inquiries must contact the author Computational Neuroscience: Large-scale neural population dynamics, brain-inspired...

MLPACK is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users. * More info + downloads: https://mlpack.org * Git repo: https://github.com/mlpack/mlpack

This Arduino library is a native Apple HomeKit accessory implementation for the ESP8266 Arduino core and works without any additional bridges. This project is mainly based on an esp-home kit for ESP-OPEN-RTOS. I ported the RTOS-based implementation of the esp-home kit to the pure Arduino environment, aimed at easy and fast building projects using Arduino IDE (or Eclipse with sloeber, PlatformIO). Enjoy the "one-key" build, "one-key" upload, and work to link various other Arduino libraries...

Nifty Sim is a high-performance nonlinear finite element solver, developed at University College London. A key feature is the option of GPU-based execution, which allows the solver to significantly out-perform equivalent commercial packages.

Copenhagen Computed Tomography Toolbox is a collection of applications and libraries for flexible and efficient CT reconstruction. The toolbox apps generally take a set of projections (X-ray intensity measurements) and filter and back project them in order to recreate the image or volume that the projections represent. The project includes both mostly informative CPU implementations and highly efficient GPU implementations. Regular releases are hosted at the Python Package Index.

...After selection of the initial frequency candidates, the algorithm passes through all their possible combinations and estimates their multi-frequency statistical significance. In the end, it prints out the set of largest frequency tuples that were still found significant. The GPU computing is implemented through CUDA and brings a significant performance increase. It is still possible to run FreDec solely on CPU, if no suitable GPU device is available in the system. See the details of the underlying theory in Baluev 2013, MNRAS, V. 436, P. 807 The description of the algorithm itself can be found in arXiv:1309.0100. ...

The Local Binary Pattern (LBP) is a texture operator that is used in several different computer vision applications and implemented in a variety of platforms. When selecting a suitable LBP implementation platform, the specific application and its requirements in terms of performance, size, energy efficiency, cost and developing time has to be carefully considered. This is a software toolbox that collects software implementations of the Local Binary Pattern operator in several platforms: - OpenCL for CPU & GPU - OpenCL for GPU (branchless) - C code optimized for ARM - OpenGL ES 2.0 shaders mobile GPUs - C code for TI C64x DSP core (branchless) - C code for TTA processor synthesis If you use the code somewhere, please cite: Bordallo López M., Nieto A., Boutellier J., Hannuksela J., and Silvén O. ...