4 Repos
Awesome GitHub RepositoriesBuffer Initialization Operations
Operations to fill GPU buffers with constant values or zero them out after allocation.
Distinct from GPU Buffer Allocators: Distinct from GPU Buffer Allocators: focuses on populating buffers with constant data after allocation, not the allocation itself.
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Awesome Buffer Initialization Operations GitHub Repositories
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- chyingp/nodejs-learning-guide
chyingp/nodejs-learning-guide
6,874Auf GitHub ansehenThis project is a learning guide and collection of study notes designed to teach Node.js backend development. It provides a comprehensive core API reference and practical demonstrations for implementing server-side logic, network programming, and system APIs. The guide specifically covers advanced technical domains including process management for scaling applications via clusters and child processes, as well as network programming for building TCP, UDP, and HTTP services. It also includes detailed instructional material on security implementation, focusing on cryptographic hashing and encryp
Provides a utility to populate a buffer with a repeating value for initialization.
Rubycryptoexpressnodejs - gfx-rs/gfx
gfx-rs/gfx
5,398Auf GitHub ansehengfx ist eine Hardware-agnostische Grafik-API-Abstraktion, die einen einheitlichen Satz von Grafik- und Compute-Befehlen in native Anweisungen für mehrere GPU-Treiber übersetzt. Sie bietet eine gemeinsame Schnittstelle für plattformübergreifendes Rendering und allgemeine GPU-Compute-Programmierung. Das Projekt bietet ein Shader-Übersetzungssystem für Zwischenrepräsentationen, das Quellcode und SPIR-V in zielspezifische Sprachen konvertiert. Es verwendet ein datengesteuertes Referenz-Test-Framework, um zu verifizieren, dass die Grafikausgabe über verschiedene Hardwareplattformen hinweg konsistent bleibt. Die Funktionen umfassen paralleles Command-Buffer-Encoding über mehrere Threads hinweg und die Kapselung von Pipeline-Zuständen in einzelne Objekte, um redundante Zustandsänderungen zu minimieren. Das System verwaltet Low-Level-GPU-Ressourcen, einschließlich Speicherallokation, asynchronem Buffer-Mapping und expliziter Frame-Präsentation via Swapchains. Die Implementierung zielt auf native Umgebungen und Webbrowser mittels WebAssembly ab und bietet Übersetzungsschichten für WebGL und WebGL2.
Automatically clears GPU buffer memory upon allocation to ensure consistent state across different hardware platforms.
Rustdx11dx12gfx - tile-ai/tilelang
tile-ai/tilelang
5,226Auf GitHub ansehenTileLang is a Python-embedded domain-specific language compiler that JIT-compiles and autotunes GPU kernels. It uses a tile-based DSL, automatic software pipelining, and parallel autotuning to generate optimized GPU kernels at runtime. It supports tensor core operations with Pythonic syntax, automatic memory management, and thread mapping. The compiler searches over tile sizes, thread counts, and scheduling policies, compiling and benchmarking candidates in parallel to find the fastest kernel. It also caches compiled binaries and tuning results to disk for reuse across sessions. TileLang inc
TVM's feature to fill every element of a buffer with a specified constant value, including a dedicated operation to zero it out.
Python - flashinfer-ai/flashinfer
flashinfer-ai/flashinfer
4,996Auf GitHub ansehenFlashInfer is a library of high-performance GPU kernels purpose-built for accelerating large language model inference. It provides optimized implementations for attention operations (including flash attention, page attention, multi-head latent attention, and cascade attention) using paged key-value caches, fused kernel composition, and just-in-time compilation. The library also includes specialized kernels for mixture-of-experts layers, block-scaled low-precision quantization (FP8, FP4), and distributed collective communication. What distinguishes FlashInfer is its fused all-reduce communicat
Initializes device buffers for distributed synchronization protocols in multi-GPU inference.
Pythonattentioncudadistributed-inference
