2 रिपॉजिटरी
Awesome GitHub RepositoriesShared Memory Swizzling
Data layout techniques that rearrange shared memory access to prevent bank conflicts.
Distinct from Shared Memory Buffers: Candidates focus on distributed memory or buffers, not internal GPU shared memory bank conflict avoidance
Explore 2 awesome GitHub repositories matching operating systems & systems programming · Shared Memory Swizzling. Refine with filters or upvote what's useful.
Awesome Shared Memory Swizzling GitHub Repositories
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- xlite-dev/leetcuda
xlite-dev/LeetCUDA
9,694GitHub पर देखेंLeetCUDA is a collection of high-performance GPU kernel libraries focusing on memory optimization, activation functions, and attention mechanisms. It serves as a reference library for CUDA kernel implementations, ranging from basic element-wise operations to complex neural network components, and provides Python bindings to integrate these kernels into deep learning workflows. The project is distinguished by its focus on low-level hardware optimizations. This includes the use of tensor cores for half-precision matrix multiplication, asynchronous data pipelining with double buffering, and shar
Prevents shared memory bank conflicts by rearranging data access patterns via swizzling.
Cudacudacuda-12cuda-cpp - tile-ai/tilelang
tile-ai/tilelang
5,226GitHub पर देखेंTileLang 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
Implements shared memory to MMA sparse layout conversion for tensor core instructions.
Python
