Search Results for "gpu max performance"
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Showing 367 open source projects for "gpu max performance"
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GPU Hot
Real-time NVIDIA GPU dashboard
GPU Hot is an open-source, lightweight monitoring dashboard designed to provide real-time visibility into NVIDIA GPU performance across single machines or entire clusters. The project offers a self-hosted web interface that streams hardware metrics directly from GPU servers, enabling developers, ML engineers, and system administrators to observe GPU utilization and system behavior in real time through a browser. -
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AGI (Android GPU Inspector)
Android GPU Inspector
Android GPU Inspector (AGI) is a desktop tool for profiling, tracing, and debugging graphics workloads running on Android devices. It helps developers analyze Vulkan and OpenGL ES applications at the system, frame, and draw-call levels to uncover GPU and CPU bottlenecks. AGI captures detailed performance counters, timelines, and pipeline state to reveal stalls, overdraw, shader hotspots, and inefficient resource usage. -
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Gupax
GUI Uniting P2Pool And XMRig
A versatile tool designed for managing and analyzing GPU performance across tasks. -
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HOOMD-blue
Molecular dynamics and Monte Carlo soft matter simulation on GPUs
...Its main value is combining a flexible Python user experience with strong GPU performance for modern particle simulation research. -
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Apple Silicon Guide
Learn all about the A17 Pro, A16 Bionic, R1, M1-series
The Apple Silicon Guide repository is a detailed resource for users adopting or migrating to Apple Silicon (M1/M2/Pro/Max/Ultra) Macs, offering a wealth of information on hardware capabilities, software compatibility, performance optimization, and tooling best practices. It covers differences between Apple Silicon and legacy Intel architectures, helping users understand how to choose software builds (arm64 vs. universal), deal with Rosetta 2 translation, and optimize workloads for native performance. ... -
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nviwatch
A blazingly fast rust based TUI for managing and monitoring NVIDIA GPU
NviWatch is an interactive terminal user interface (TUI) application for monitoring NVIDIA GPU devices and processes. Built with Rust, it provides real-time insights into GPU performance metrics, including temperature, utilization, memory usage, and power consumption. -
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Alacritty
A cross-platform, GPU-accelerated terminal emulator
Alacritty is the fastest open source terminal emulator there is. How is it the fastest? With such a strong focus on simplicity and performance, Alacritty’s included features are very carefully considered, ensuring that it remains blazingly fast. It’s got a GPU for rendering that makes a whole lot of optimizations possible. In various benchmarked terminals, Alacritty has shown to be either faster, or way faster than others. Alacritty requires no additional setup, but still allows configuration of many aspects of the terminal. ... -
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XMRig
RandomX, KawPow, CryptoNight, AstroBWT and GhostRider unified miner
High performance, open-source, cross-platform RandomX, KawPow, CryptoNight, and AstroBWT CPU/GPU miner, RandomX benchmark, and stratum proxy. XMRig is a high-performance, open-source, cross-platform RandomX, KawPow, CryptoNight, and AstroBWT unified CPU/GPU miner and RandomX benchmark. Official binaries are available for Windows, Linux, macOS, and FreeBSD. -
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how-to-optim-algorithm-in-cuda
How to optimize some algorithm in cuda
...These examples show how different optimization techniques influence performance on modern GPU hardware and allow readers to experiment with real implementations. The repository also contains extensive learning notes that summarize CUDA programming concepts, GPU architecture details, and performance engineering strategies. -
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ParallelStencil.jl
Package for writing high-level code for parallel stencil computations
ParallelStencil empowers domain scientists to write architecture-agnostic high-level code for parallel high-performance stencil computations on GPUs and CPUs. Performance similar to CUDA C / HIP can be achieved, which is typically a large improvement over the performance reached when using only CUDA.jl or AMDGPU.jl GPU Array programming. 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. ... -
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SwiftShader
SwiftShader is a high-performance CPU-based implementation
SwiftShader is Google’s high-performance CPU-based implementation of the Vulkan 1.3 graphics API, designed to provide a hardware-independent rendering solution for 3D graphics. Unlike traditional GPU drivers, SwiftShader executes graphics commands entirely on the CPU, making it ideal for environments where dedicated graphics hardware is unavailable or unsuitable. It acts as a drop-in replacement for Vulkan drivers, allowing existing applications to run seamlessly by redirecting API calls through its software-based rendering engine. ... -
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CubeCL
Multi-platform high-performance compute language extension for Rust
CubeCL is a low-level compute language and compiler framework designed to simplify and optimize GPU programming for high-performance workloads, particularly in machine learning and numerical computing. It provides an abstraction layer that allows developers to write portable, hardware-efficient compute kernels without directly dealing with complex GPU APIs such as CUDA or OpenCL. CubeCL focuses on delivering predictable performance and composability by exposing explicit control over memory layouts, parallelism, and execution patterns while still maintaining a developer-friendly syntax. ... -
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PowerInfer
High-speed Large Language Model Serving for Local Deployment
PowerInfer is a high-performance inference engine designed to run large language models efficiently on personal computers equipped with consumer-grade GPUs. The project focuses on improving the performance of local AI inference by optimizing how neural network computations are distributed between CPU and GPU resources. Its architecture exploits the observation that only a subset of neurons in large models are frequently activated, allowing the system to preload frequently used neurons into GPU memory while processing less common activations on the CPU. ... -
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Fabulously Optimized
A simple Minecraft modpack focusing on performance and graphics
A simple Minecraft modpack focusing on performance and graphics enhancements. A Minecraft modpack focused on performance, providing a smooth experience with multiple optimization mods. -
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llmfit
157 models, 30 providers, one command to find what runs on hardware
llmfit is a terminal-based utility that helps developers determine which large language models can realistically run on their local hardware by analyzing system resources and model requirements. The tool automatically detects CPU, RAM, GPU, and VRAM specifications, then ranks available models based on performance factors such as speed, quality, and memory fit. It provides both an interactive terminal user interface and a traditional CLI mode, enabling flexible workflows for different user preferences. llmfit also supports advanced configurations including multi-GPU setups, mixture-of-experts architectures, and dynamic quantization recommendations. ... -
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luma.gl
High-performance Toolkit for WebGL-based data visualization
luma.gl is a GPU toolkit for the Web-focused primarily on data visualization use cases. luma.gl aims to provide support for GPU programmers that need to work directly with shaders and want a low abstraction API that remains conceptually close to the WebGPU and WebGL APIs. Unlike other common WebGL APIs, the developer can choose to use the parts of luma.gl that support their use case and leave the others behind. While generic enough to be used for general 3D rendering, luma.gl's mandate is... -
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NVIDIA Profile Inspector
Modify game profiles inside the internal driver database
NVIDIA Profile Inspector is a specialized utility that allows users to view and modify hidden graphics driver settings within NVIDIA’s internal profile database, providing deeper control than the official NVIDIA Control Panel. It exposes advanced and undocumented configuration options that can influence rendering behavior, performance optimization, and compatibility for specific games. Users can create, edit, and assign profiles for individual applications, enabling fine-grained tuning of GPU behavior beyond standard settings. The tool is particularly popular among enthusiasts who want to optimize performance, troubleshoot graphical issues, or enable experimental features such as custom DLSS configurations. ... -
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XenosRecomp
A tool for converting Xbox 360 shaders to HLSL
...The project addresses one of the most complex aspects of console reverse engineering, which is accurately reproducing proprietary GPU behavior in a portable and efficient way. By reconstructing the graphics pipeline, XenosRecomp enables developers to render scenes correctly without relying on emulation layers that can introduce performance overhead or inaccuracies. -
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CUDA Python
Performance meets Productivity
CUDA Python is a unified Python interface for accessing and working with the NVIDIA CUDA platform, enabling developers to build GPU-accelerated applications entirely in Python. It acts as a metapackage composed of multiple submodules that provide both high-level and low-level access to CUDA functionality, including runtime APIs, driver APIs, and JIT compilation tools. The project is designed to simplify GPU programming by offering Pythonic abstractions while still exposing the full power of... -
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KonaBess
A GPU overclock & undervolt tool for various Snapdragon chips
KonaBess is a straightforward application designed to customize GPU frequency and voltage tables without the need for kernel recompilation. The application achieves customization by unpacking the Boot/Vendor Boot image, decompiling and editing relevant dtb (device tree binary) files, and finally repacking and flashing the modified image. The extent of improvement varies, with some users reporting a 25% reduction in power consumption in the graphics benchmark (4.2w->3.2w) after undervolting... -
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Citron Neo
Research software designed to orchestrate virtual environments
Citron Neo is an advanced emulator project focused on replicating complex system environments with high performance and flexibility. It is designed to emulate modern console behavior while integrating improvements in CPU emulation, GPU rendering, and memory management. The project incorporates optimizations such as dynamic recompilation and Vulkan-based rendering to enhance performance across supported platforms. It also includes continuous updates that improve compatibility with games and system firmware, reflecting an active development cycle. ... -
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CUDA Core Compute Libraries (CCCL)
CUDA Core Compute Libraries
...By unifying these components, CCCL reduces duplication and improves developer productivity while maintaining performance across different GPU architectures. -
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Flash-MoE
Running a big model on a small laptop
...It likely includes support for GPU acceleration and parallel processing, enabling it to handle large-scale workloads effectively. The architecture emphasizes speed and efficiency, making it suitable for both research and production environments where performance is critical. It may also provide tools for benchmarking and tuning model behavior. Overall, flash-moe represents a technical advancement in making MoE models more practical and deployable. -
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CatBoost
High-performance library for gradient boosting on decision trees
CatBoost is a fast, high-performance open source library for gradient boosting on decision trees. It is a machine learning method with plenty of applications, including ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. CatBoost offers superior performance over other GBDT libraries on many datasets, and has several superb features. -
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NVIDIA Warp
A Python framework for accelerated simulation, data generation
NVIDIA Warp is a high-performance Python framework developed by NVIDIA for building and accelerating simulation, graphics, and physics-based workloads using GPU computing. It enables developers to write kernel-level code in Python that is automatically compiled into efficient CUDA kernels, combining ease of use with near-native performance. The framework is designed for applications such as robotics, reinforcement learning, physical simulation, and differentiable computing, where performance and flexibility are critical. ...
