Search Results for "gpu processor"
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Showing 10 open source projects for "gpu processor"
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DAT Freight and Analytics - DATDAT Freight & Analytics operates DAT One, North America’s largest truckload freight marketplace; DAT iQ, the industry’s leading freight data analytics service; and Trucker Tools, the leader in load visibility. Shippers, transportation brokers, carriers, news organizations, and industry analysts rely on DAT for market trends and data insights, informed by nearly 700,000 daily load posts and a database exceeding $1 trillion in freight market transactions. Founded in 1978, DAT is a business unit of Roper Technologies (Nasdaq: ROP), a constituent of the Nasdaq 100, S&P 500, and Fortune 1000. Headquartered in Beaverton, Ore., DAT continues to set the standard for innovation in the trucking and logistics industry.
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Perfetto
Production-grade client-side tracing, profiling, and analysis
...It’s designed around a low-overhead producer/consumer model: instrumented components (“producers”) write binary events into shared memory buffers and a collector (“service”) reliably streams them to storage. The data model spans kernel and userspace, so you can stitch together CPU scheduling, app lifecycles, binder/IPC hops, GPU work, power and thermal signals, file I/O, heap samples, and more into a single coherent timeline. Perfetto’s ecosystem includes a web-based UI that can load multi-GB traces directly in the browser and an offline “trace processor” that exposes the trace as a queryable SQL-like table schema for deep analysis and automation. It integrates natively with Android (replacing older atrace/Systrace paths) and can sit alongside custom instrumentation in apps or services through its SDKs. -
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OpenVINO
OpenVINO™ Toolkit repository
...Reduce resource demands and efficiently deploy on a range of Intel® platforms from edge to cloud. This open-source version includes several components: namely Model Optimizer, OpenVINO™ Runtime, Post-Training Optimization Tool, as well as CPU, GPU, MYRIAD, multi device and heterogeneous plugins to accelerate deep learning inferencing on Intel® CPUs and Intel® Processor Graphics. It supports pre-trained models from the Open Model Zoo, along with 100+ open source and public models in popular formats such as TensorFlow, ONNX, PaddlePaddle, MXNet, Caffe, Kaldi. -
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oneDNN
oneAPI Deep Neural Network Library (oneDNN)
This software was previously known as Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) and Deep Neural Network Library (DNNL). oneAPI Deep Neural Network Library (oneDNN) is an open-source cross-platform performance library of basic building blocks for deep learning applications. oneDNN is part of oneAPI. The library is optimized for Intel(R) Architecture Processors, Intel Processor Graphics and Xe Architecture graphics. oneDNN has experimental support for the following architectures: Arm* 64-bit Architecture (AArch64), NVIDIA* GPU, OpenPOWER* Power ISA (PPC64), IBMz* (s390x), and RISC-V. oneDNN is intended for deep learning applications and framework developers interested in improving application performance on Intel CPUs and GPUs. ... -
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powerMAX
powerMAX is a CPU and GPU burn-in test
powerMAX is a CPU and GPU burn-in tool designed to push your hardware to its absolute thermal and power limits. It helps users uncover stability issues, cooling weaknesses, and power delivery problems by applying maximum, sustained stress to both the processor and graphics card. The utility supports dedicated CPU tests—SSE or AVX—and a demanding GPU 3D rendering test, with the option to run both simultaneously for full-system power load evaluation. -
Rent Manager SoftwareRent Manager is award-winning property management software built for residential, commercial, and short-term-stay portfolios of any size. The program’s fully customizable features include a double-entry accounting system, maintenance management/scheduling, marketing integration, mobile applications, more than 450 insightful reports, and an API that integrates with the best PropTech providers on the market.
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PDP-OmniSim
PDP-OmniSim simulating parallel and distributed processing systems
PDP-OmniSim 🧬 Scientific Overview PDP-OmniSim is an advanced computational framework for simulating parallel and distributed processing systems, with cutting-edge applications in computational neuroscience, distributed computing, and complex systems modeling. The framework provides researchers with robust tools for large-scale simulations of networked systems and their emergent behaviors. 🎯 Key Scientific Contributions 🔬 Interdisciplinary Research Domains Computational... -
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AMD APU Tuning Utility
Unlock the full potential of your Zen-based APU with AMD APU Tuning
...Universal x86 Tuning Utility serves as a lightweight alternative to well-known software like Ryzen Master and Intel XTU (Extreme Tuning Utility). One of the main features of the utility is the fine-grained control it offers over the device's processor and GPU(s). Users can access a wide range of settings, allowing them to adjust clock speeds, power limits, voltages, and other performance-related parameters. -
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SageMaker TensorFlow Serving Container
A TensorFlow Serving solution for use in SageMaker
...Amazon SageMaker uses Docker containers to run all training jobs and inference endpoints. The Docker images are built from the Dockerfiles in docker/. The Dockerfiles are grouped based on the version of TensorFlow Serving they support. Each supported processor type (e.g. "cpu", "gpu", "ei") has a different Dockerfile in each group. If your are testing locally, building the image is enough. But if you want to your updated image in SageMaker, you need to publish it to an ECR repository in your account. You can also run your container locally in Docker to test different models and input inference requests by hand. -
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SageMaker Chainer Containers
Docker container for running Chainer scripts to train and host Chainer
SageMaker Chainer Containers is an open-source library for making the Chainer framework run on Amazon SageMaker. This repository also contains Dockerfiles which install this library, Chainer, and dependencies for building SageMaker Chainer images. Amazon SageMaker utilizes Docker containers to run all training jobs & inference endpoints. The Docker images are built from the Dockerfiles specified in Docker/. The Docker files are grouped based on Chainer version and separated based on Python... -
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GENIE (GEne-geNe IntEraction)
GPU based Parallel Gene-Gene Interaction Analysis
...However, currently there are no genetic analysis software packages that allow users to fully utilize the computing power of these multi-core devices for genetic interaction analysis for binary traits. Here we present a novel software package GENIE, which utilizes the power of multiple GPU or CPU processor cores to parallelize the interaction analysis. Citation: Chikkagoudar, S., Wang, K., & Li, M. (2011). GENIE: a software package for gene-gene interaction analysis in genetic association studies using multiple GPU or CPU cores. BMC research notes, 4(1), 158. -
AestheticsPro Medical Spa SoftwareAestheticsPro is the most complete Aesthetics Software on the market today. HIPAA Cloud Compliant with electronic charting, integrated POS, targeted marketing and results driven reporting; AestheticsPro delivers the tools you need to manage your medical spa business. It is our mission To Provide an All-in-One Cutting Edge Software to the Aesthetics Industry.
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10
LBP in multiple platforms
LBP implementation in multiple computing platforms (ARM,GPU, DSP...)
...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. "Evaluation of real-time LBP computing in multiple architectures," Journal of Real Time Image Processing, 2014
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