Search Results for "nvidia"
Sort By:
Showing 13 open source projects for "nvidia"
View related business solutions-
99.99% Uptime for MySQL and PostgreSQL DatabasesCloud SQL Enterprise Plus delivers near-zero downtime with 35 days of point-in-time recovery. Supports MySQL, PostgreSQL, and SQL Server.
-
Paessler: Easy to Use With Enterprise Power. Free TrialYou shouldn't have to choose between a monitoring tool that's easy to use and one that's powerful enough for a complex environment. PRTG's low-code interface lets any admin build dashboards, set alerts and monitor devices without scripting, while custom sensors and full API access are there when your team needs deeper control. One platform, no compromise. Download a free 30-day trial now.
-
1
Zenith
Sort of like top or htop but with zoom-able charts, CPU, GPU
...Install "musl-tools" package on debian/ubuntu derivatives, "musl-gcc" on fedora and equivalent on other distributions from their standard repos. If one needs to build with NVIDIA support in a virtual environment, then it requires some more setup since typically the VM software is unable to directly expose NVIDIA GPU. Unlike the runtime zenith script, the Makefile has been setup to detect only the presence of required NVIDIA libraries, so it is possible to build with NVIDIA support even when without NVIDIA GPU. -
2
CUDA.jl
CUDA programming in Julia
High-performance GPU programming in a high-level language. JuliaGPU is a GitHub organization created to unify the many packages for programming GPUs in Julia. With its high-level syntax and flexible compiler, Julia is well-positioned to productively program hardware accelerators like GPUs without sacrificing performance. The latest development version of CUDA.jl requires Julia 1.8 or higher. If you are using an older version of Julia, you need to use a previous version of CUDA.jl. This will... -
3
ParallelStencil.jl
Package for writing high-level code for parallel stencil computations
...For example, a 2-D shallow ice solver presented at JuliaCon 2020 [1] achieved a nearly 20 times better performance than a corresponding GPU Array programming implementation; in absolute terms, it reached 70% of the theoretical upper performance bound of the used Nvidia P100 GPU, as defined by the effective throughput metric, T_eff. ParallelStencil relies on the native kernel programming capabilities of CUDA.jl and AMDGPU.jl and on Base.Threads for high-performance computations on GPUs and CPUs, respectively. It is seamlessly interoperable with ImplicitGlobalGrid.jl, which renders the distributed parallelization of stencil-based GPU and CPU apps. -
4
NVIDIA Merlin
Library providing end-to-end GPU-accelerated recommender systems
...For more information, see NVIDIA Merlin on the NVIDIA developer website. Transform data (ETL) for preprocessing and engineering features. Accelerate your existing training pipelines in TensorFlow, PyTorch, or FastAI by leveraging optimized, custom-built data loaders. Scale large deep learning recommender models by distributing large embedding tables that exceed available GPU and CPU memory. -
$300 Free Credits to Build on Google CloudPut your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
-
5
bitResurrector
Bitcoin Seed Phrase Recovery & Private Key Finder: GPU & Bloom Filter
BitResurrector v3.0.3 is a high-performance offline tool for recovering forgotten Bitcoin assets, damaged seed phrases, and vulnerable private keys. KEY CAPABILITIES: 1. BIP-39 Seed Phrase Recovery - Scrambled Order: Solves 12! word permutations in 1–2 hours via rapid checksum pruning. - Missing Words: Fast brute-force for 3–4 lost or damaged words with Secp256k1 derivation. 2. Real-Time Database Matching - Zero-Latency Bloom Filter: Instant lookup across 50M+ active funded... -
6
CUDAnative.jl
Julia support for native CUDA programming
The programming support for NVIDIA GPUs in Julia is provided by the CUDA.jl package. It is built on the CUDA toolkit and aims to be as full-featured and offer the same performance as CUDA C. The toolchain is mature, has been under development since 2014, and can easily be installed on any current version of Julia using the integrated package manager. -
7
DICOM Image Reader
A minimal Medical application to read and view DICOM (dcm) image file
...It is based DWV JavaScript opensource library. =============== Requirements: - Processor: Intel Pentium 4 processor or later that's SSE2 capable - RAM: 512 MB - Hard Disk: 1500 MB - Video Card: nVidia, ATI and Intel Shader Model 2 and Shader Model 3 video cards with 512 megabytes of RAM minimum - Supported OS: Windows 10, Windows 8, Windows 8.1, Windows 7 =============== Alternative software for Windows: Available on Ecwid - https://goo.gl/twBcAq =============== Android App: https://goo.gl/i4NX9K -------------- Chrome app Release Pro: https://goo.gl/p456N5 -
8
WhirlwindDB
GPU Analytic Database
A SQL based analytic engine running on an NVIDIA GPU for exceptional performance. We see over 700x performance increase over a well known database on the same machine. -
9
A shareware for build realtime photoacoustic tomography image using NVidia Geforce GPU. Can work in both offline mode or online mode.
-
Already Paying for an RMM? Get 50% Off Splashtop AEMAutomated OS and third-party patching for Windows and macOS, CVE prioritization with AI patch summaries, and built-in remote control that also supports Linux. Qualifying organizations switching from another RMM get up to 50% off year one.
-
10
This Eclipse plugin help you to create, edit and verify your GLSL (OpenGL Shading Language) and NVidia CG vertex and pixel shaders directly inside the Eclipse IDE. Each shader can be edit in an editor with syntax color with error/warning markers.
-
11
GPU Edge Detector
GPU Interactive Program For Edge detection
...(The code section is currently close until our article gets published) Commiting publicly to the project is currently closed but feel free to email us. The source code is dependant on CUDA 5.0 Samples now available as part of the CUDA Toolkit. "This software contains source code provided by NVIDIA Corporation." -
12
All pairs n-body simulation of electromagnetic forces on charged particles, adapted from Nvidia SDK 3.2 sample. Uses Lorentz force from EM fields via the Biot-Savart law for moving charged particles (uses relative velocity). Relativistic.
-
13
This project shows how to integrate NVIDIA CUDA GPU programming API into ITK (Insight Segmentation and Registration Toolkit) library
- Previous
- You're on page 1
- Next
