cuDNN Frontend is NVIDIA's modern, open-source entry point to the cuDNN library and a growing collection of high-performance open-source kernels.

Memory-optimized SongGeneration (v2 Large) for 16GB VRAM GPUs. Features 8-bit µ-law KV-caching, fused layers, and SDPA/Triton integration.

An open-source interface to use the multiple-precision solver SDPA-GMP with YALMIP

PyTorch implementation of YOLOv12 with Scaled Dot-Product Attention (SDPA) optimized by FlashAttention for fast and efficient object detection.

A 66M parameter decoder-only transformer language model implemented from scratch in PyTorch. Features a custom SentencePiece tokenizer, RoPE positional embeddings, SwiGLU feed-forward network, per-layer KV cache for efficient autoregressive inference, and a Svelte-based streaming chat interface.

Signed attention for transformers — sinh/cosh attention giving weights in [-1,1] instead of softmax's positive-only, made FlashAttention/SDPA-compatible by channel doubling. Includes a from-scratch softmax-vs-SBA comparison and a documented negative result.

Long-context benchmark pushing Qwen2-0.5B from 4K to 32K tokens on RTX 2070 using SDPA + chunked prefill. Shows 40x speedup at 8K, FP16 beating INT4 at long context, and that quantization is NOT a long-context solution — KV-cache is the real bottleneck.

Fork of sdpa-qd (quad-double SDP solver): first working OpenMP threading (1.5x), plus a fixed benchmark clock - upstream reported CPU time as elapsed, making speedups look like slowdowns.

OpenMP-threaded fork of sdpa-gmp (arbitrary-precision SDP solver). 2.4x at 256-bit precision, deterministic; fixes cases where upstream threading was slower than serial.

Attention backend benchmark on Turing GPUs comparing Vanilla, SDPA Math, SDPA Efficient, and a custom Triton FlashAttention implementation. SDPA efficient achieves 130× memory reduction and 10× speedup; Triton FA achieves O(n) memory but is 64× slower than SDPA efficient on RTX 2070.

OpenMP-threaded, reproducible fork of sdpa-dd (double-double SDP solver). 2.3x over upstream master, bitwise identical results at any thread count.