4 个仓库
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.
Explore 4 awesome GitHub repositories matching data & databases · Buffer Initialization Operations. Refine with filters or upvote what's useful.
Awesome Buffer Initialization Operations GitHub Repositories
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- chyingp/nodejs-learning-guide
chyingp/nodejs-learning-guide
6,874在 GitHub 上查看This 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
5,398在 GitHub 上查看gfx 是一个硬件无关的图形 API 抽象，将一组统一的图形和计算命令转换为多个 GPU 驱动程序的原生指令。它为跨平台渲染和通用 GPU 计算编程提供了一个通用接口。 该项目具有一个中间表示（IR）着色器翻译系统，可将源代码和 SPIR-V 转换为目标特定语言。它采用数据驱动的参考测试框架来验证图形输出在不同硬件平台上保持一致。 功能包括跨多个线程的并行命令缓冲区编码，以及将管线状态封装到单个对象中以最大限度地减少冗余状态更改。该系统管理底层 GPU 资源，包括内存分配、异步缓冲区映射以及通过交换链（swapchains）进行的显式帧呈现。 该实现通过 WebAssembly 针对原生环境和 Web 浏览器，为 WebGL 和 WebGL2 提供翻译层。
Automatically clears GPU buffer memory upon allocation to ensure consistent state across different hardware platforms.
Rustdx11dx12gfx - tile-ai/tilelang
tile-ai/tilelang
5,226在 GitHub 上查看TileLang 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
4,996在 GitHub 上查看FlashInfer 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
