22 रिपॉजिटरी
General-purpose data processing on graphics hardware.
Explore 22 awesome GitHub repositories matching hardware & iot · GPU Computations. Refine with filters or upvote what's useful.
This project is a high-level 3D graphics engine designed to render complex, hardware-accelerated environments within web browsers. It provides a comprehensive abstraction layer that manages scene graphs, cameras, and lighting, mapping high-level scene definitions onto low-level graphics APIs. By decoupling these definitions from specific hardware targets, the engine ensures consistent performance across diverse browsers and devices. The framework distinguishes itself through a robust architecture that includes a unified math library for high-frequency spatial calculations and a physically bas
Executes general-purpose data processing tasks directly on graphics hardware to accelerate complex mathematical calculations.
This project is a cross-platform graphics and compute framework that provides a unified, hardware-agnostic abstraction layer for rendering and parallel processing. It enables developers to build high-performance applications that execute consistently across diverse operating systems and hardware backends, including Vulkan, Metal, and DirectX. By mapping high-level graphics commands to native APIs, it serves as a portable foundation for both real-time 3D rendering and general-purpose GPU computing. The framework distinguishes itself through a robust architecture that supports both native deskt
A parallel processing interface that allows developers to execute general-purpose code and complex mathematical calculations directly on graphics hardware.
bgfx is a cross-platform, graphics rendering abstraction layer designed for high-performance applications. It provides a unified interface that maps high-level rendering commands to native graphics APIs, allowing developers to maintain a single codebase that executes consistently across diverse operating systems and hardware architectures. The library distinguishes itself through a multi-threaded command submission model that decouples rendering logic from the main application thread, effectively minimizing CPU bottlenecks. It utilizes a backend-agnostic command buffer and a deferred resource
Executes general-purpose parallel calculations and offline rendering tasks on the graphics processor without requiring a display window.
ZLUDA is a middleware and translation engine designed to enable the execution of unmodified proprietary compute binaries on non-native graphics hardware. It functions as a compatibility layer that bridges vendor-specific compute interfaces with open standards, allowing software originally restricted to a single hardware ecosystem to operate on alternative graphics processing units. The project achieves this through a combination of dynamic library interception and runtime instruction translation. By replacing standard system libraries and mapping proprietary compute calls to open standards, t
Enables high-performance compute applications to execute on diverse hardware by translating proprietary instructions at runtime.
Cpp-taskflow is a C++ task-parallelism framework and task graph scheduler designed to manage and execute complex dependency graphs of parallel tasks across CPU and GPU hardware. It provides a parallel algorithm library for high-performance implementations of reductions, sorts, pipelines, and iterations. The framework distinguishes itself through its ability to offload heavy computational workloads from a task graph to graphics processors for acceleration. It also includes a task profiling tool and a performance analysis interface for visualizing task execution flow and dependency structures t
Accelerates scientific workloads by executing general-purpose parallel calculations on the GPU without a display window.
This project serves as a comprehensive educational framework and reference library for mastering high-performance graphics programming and parallel compute resource management. It provides a collection of practical implementations designed to demonstrate the explicit control required by the Vulkan API, covering the fundamental mechanics of modern graphics pipelines and cross-platform hardware interaction. The repository distinguishes itself by focusing on the low-level architectural requirements of modern GPU development, including manual memory allocation, multi-threaded command recording, a
Provides functional implementations for executing parallel data processing tasks and compute-driven geometry operations on graphics hardware.
Numba एक जस्ट-इन-टाइम कंपाइलर है जो हाई-लेवल Python फंक्शन्स को रनटाइम पर ऑप्टिमाइज़्ड मशीन कोड में अनुवादित करता है। LLVM कंपाइलर इंफ्रास्ट्रक्चर का लाभ उठाकर, यह संख्यात्मक डेटा प्रोसेसिंग और गणितीय गणनाओं में तेजी लाने के लिए एक ढांचा प्रदान करता है, जो स्टेटिकली कंपाइल की गई भाषाओं के बराबर प्रदर्शन स्तर को सक्षम बनाता है। यह प्रोजेक्ट टाइप-इन्फरेंस-आधारित स्पेशलाइजेशन के माध्यम से खुद को अलग करता है, जो निष्पादन के दौरान उपयोग किए जाने वाले विशिष्ट डेटा प्रकारों के अनुरूप मशीन निर्देश उत्पन्न करता है। यह एक लेज़ी कंपाइलेशन पाइपलाइन का उपयोग करता है जो इनवोकेशन के क्षण तक अनुवाद को स्थगित कर देता है, जिससे स्टार्टअप ओवरहेड कम हो जाता है और विविध प्रोसेसर आर्किटेक्चर और ऑपरेटिंग सिस्टम में लगातार प्रदर्शन बना रहता है। कोर कंपाइलेशन के अलावा, यह टूलकिट कई CPU कोर और ग्राफिक्स प्रोसेसिंग यूनिट्स में पुनरावृत्ति संचालन (iterative operations) और ऐरे एक्सप्रेशन्स को वितरित करके हार्डवेयर एक्सेलेरेशन के लिए व्यापक समर्थन प्रदान करता है। यह बड़े पैमाने के संख्यात्मक डेटासेट के लिए थ्रूपुट को अधिकतम करने के लिए वेक्टरइज़ेशन और पैरेललइज़ेशन रणनीतियों का उपयोग करता है, जिससे डेवलपर्स सीधे स्टैंडर्ड कोड से विशेष हार्डवेयर को लक्षित कर सकते हैं।
Executes parallel algorithms on graphics hardware by targeting processing units directly within standard code.
Sokol is a C hardware abstraction layer and cross-platform graphics library designed for managing windowing, input, and audio across different operating systems. It functions as a GPU resource manager and multimedia application framework, providing a unified API for rendering 2D and 3D graphics across WebGL, Metal, Direct3D, and OpenGL. The project is distinguished by its single-header implementation, which simplifies integration and portability. It utilizes a stateless render pass definition and a one-update-per-frame model to synchronize CPU data to GPU memory and manage resource lifecycles
Enables general-purpose data processing on the GPU using compute shaders and storage buffers.
jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU hardware. Its primary purpose is to enable real-time computer vision and AI inference at the edge with low latency and high throughput. The project distinguishes itself through high-performance streaming analytics and the ability to execute concurrent AI pipelines on auto-grade silicon. It provides specialized support for multi-sensor stream processing, utilizing zero-copy data transport to load camera frames directly into GPU memory. The codebase covers a broad surface of capabiliti
Leverages parallel processing power on GPUs to execute computationally intensive tasks through Python applications.
This project is a collection of reference implementations and technical guides for building high-performance 3D applications and graphics experiments on Windows. It provides a library of samples covering the implementation of GPU compute frameworks, raytracing reference models, and shader optimization techniques. The repository includes specific demonstrations for modeling physical light behavior to create reflections and lighting effects, as well as tools for analyzing memory dumps and tracking real-time execution metrics on graphics hardware. It further provides guidance on managing shader
Offloads heavy mathematical processing to graphics hardware using compute shaders and work graphs.
Offloads pipeline stages to GPU compute APIs such as CUDA, Metal, or DirectX 12.
Runs parallel workloads on NVIDIA hardware using a programming model and libraries for GPU computation.
Gorgonia is a Go library that provides an automatic differentiation engine and a computation graph framework for building and training neural networks. It functions as a CUDA-accelerated tensor library and a SIMD-optimized math library, enabling machine learning workflows entirely within the Go ecosystem. The library distinguishes itself through a dual-backend architecture that dispatches neural network operations to either a GPU or CPU depending on CUDA availability at runtime. It constructs differentiable directed acyclic graphs of tensor operations, supports reverse-mode automatic gradient
Creates hardware-specific CUDA bindings using a code generation tool to enable GPU-accelerated computation.
gfx is a hardware-agnostic graphics API abstraction that translates a unified set of graphics and compute commands into native instructions for multiple GPU drivers. It provides a common interface for cross-platform rendering and general-purpose GPU compute programming. The project features an intermediate-representation shader translation system that converts source code and SPIR-V into target-specific languages. It employs a data-driven reference test framework to verify that graphics output remains consistent across different hardware platforms. Capabilities include parallel command buffe
Enables execution of general-purpose computing tasks and data processing workloads on GPUs across different platforms.
This project is a collection of reference implementations and benchmarks demonstrating the use of the Vulkan graphics and compute API. It provides a set of cross-platform examples and GPU programming patterns designed for high-performance rendering and hardware-accelerated tasks. The repository includes a suite of performance benchmarks used to measure API behavior across different hardware environments. It features a modular architecture that organizes rendering examples into isolated units, along with command-line utilities for the batch execution of sample sequences. The project covers se
Executes compute tasks and captures output in environments without a physical display.
Orillusion ब्राउज़र में उच्च-निष्ठा दृश्यों और विज़ुअल प्रभावों के लिए डिज़ाइन किया गया एक WebGPU 3D रेंडरिंग इंजन है। यह समानांतर गणितीय संचालन के लिए एक GPU कंप्यूट फ्रेमवर्क और यथार्थवादी सामग्री और सतहों के लिए एक भौतिक-आधारित रेंडरिंग ग्राफिक्स पाइपलाइन के रूप में कार्य करता है। सिस्टम में कंकाल एनिमेशन को चलाने और वर्टेक्स स्थितियों को इंटरपोलेट करने के लिए एक वेब-आधारित 3D एनिमेशन टूलकिट भी शामिल है। इंजन दृश्य लॉजिक के लिए एक एंटिटी घटक सिस्टम और एक मैक्रो-आधारित शेडर जनरेशन सिस्टम के उपयोग से प्रतिष्ठित है जो कई शेडर वेरिएंट बनाता है। यह CPU और GPU के बीच डेटा एक्सचेंज ओवरहेड को कम करने के लिए मेमोरी-मैप्ड बफ़र प्रबंधन के माध्यम से प्रदर्शन को अनुकूलित करता है। यह प्रोजेक्ट क्लस्टर्ड फॉरवर्ड रेंडरिंग, प्रोब-आधारित ग्लोबल इल्यूमिनेशन, और वॉल्यूमेट्रिक गॉड रेज़ और डेप्थ ऑफ़ फील्ड जैसे प्रभावों के साथ एक पोस्ट-प्रोसेसिंग पाइपलाइन सहित ग्राफिक्स क्षमताओं की एक विस्तृत श्रृंखला को कवर करता है। यह कणों और तरल पदार्थों के लिए GPU-त्वरित भौतिकी, टक्कर का पता लगाने, और हार्डवेयर-त्वरित यूजर इंटरफेस को और एकीकृत करता है।
Runs complex mathematical operations and GPGPU tasks outside the standard graphics rendering pipeline.
LWJGL is a cross-platform library that provides Java bindings to native APIs for graphics, audio, compute, windowing, and input. It enables Java applications to access low-level hardware-accelerated capabilities such as OpenGL and Vulkan rendering, OpenAL 3D audio, OpenCL GPU compute, and GLFW windowing and input handling. Under the hood, LWJGL dynamically resolves native function pointers at runtime, loads platform-specific shared libraries, and uses generated JNI bindings to bridge Java and native code. It offers explicit memory management through direct buffer access and stack-allocated me
Offloads parallel computation tasks to GPUs via OpenCL or Vulkan compute from a Java runtime.
यह प्रोजेक्ट एक GPU फ़ीचर संगतता परत और फ़्रेम जनरेशन मॉड है। यह एक DLL API एमुलेटर और रैपर के रूप में कार्य करता है जो प्रोप्राइटरी Nvidia DLSS फ़्रेम जनरेशन कॉल्स को इंटरसेप्ट करता है और उन्हें एक वैकल्पिक कार्यान्वयन पर रीडायरेक्ट करता है। यह सॉफ़्टवेयर उन गेम्स में AMD FSR 3 फ़्रेम जनरेशन को सक्षम बनाता है जो मूल रूप से केवल Nvidia DLSS फ़्रेम जनरेशन का समर्थन करते हैं। यह प्रोप्राइटरी ग्राफिक्स अनुरोधों को एक ओपन स्टैंडर्ड में अनुवादित करता है, जिससे फ़्रेम इंटरपोलेशन उन ग्राफिक्स कार्ड्स पर चलने की अनुमति मिलती है जो अन्यथा मूल वेंडर मानकों के साथ असंगत हैं। सिस्टम अपेक्षित फ़ंक्शन सिग्नेचर्स की नकल करने और एप्लिकेशन क्रैश को रोकने के लिए DLL प्रॉक्सी रैपिंग और डायनामिक लाइब्रेरी रीडायरेक्शन का उपयोग करता है। यह प्रक्रिया रनटाइम हुकिंग और API सतह एमुलेशन के माध्यम से प्रोप्राइटरी-से-ओपन अनुवादों को मैप करती है।
Translates proprietary DLSS frame generation requests into AMD FSR 3 compatible calls.
HIP is a C++ GPU kernel language and cross-platform runtime designed for writing portable high-performance compute applications. It provides a programming interface that allows a single source codebase to execute on both AMD and NVIDIA GPU architectures. The project functions as a compatibility layer that enables the conversion and migration of existing CUDA source code to run on AMD hardware. This is achieved through a syntax mapping that mirrors CUDA and a source-to-source translation process during compilation. The toolkit covers the broader surface of cross-platform GPGPU development, in
Functions as a cross-platform runtime allowing compute applications to run on different GPU manufacturers.
oneDNN is a cross-architecture compute library and hardware acceleration framework designed as a oneAPI deep learning library. It functions as a neural network inference engine that provides optimized primitives to accelerate deep learning operations across diverse CPU and GPU architectures. The project distinguishes itself through a combination of just-in-time instruction generation based on detected processor features and microarchitecture-specific tuning. It utilizes graph-based operation compilation to minimize overhead and manages layout-aware tensors to optimize data access patterns acr
Provides a set of high-performance compute building blocks for AI deployment across different hardware vendors and runtimes.