From-scratch Ring-AllReduce on CPU (C++20) and GPU (CUDA, multi-device): real multi-GPU benchmarks, Nsight profiling, and a bandwidth-optimal ring implementation built without NCCL.
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Updated
Jul 31, 2026 - Cuda
From-scratch Ring-AllReduce on CPU (C++20) and GPU (CUDA, multi-device): real multi-GPU benchmarks, Nsight profiling, and a bandwidth-optimal ring implementation built without NCCL.
From-scratch data-parallel training framework with fault injection and continuous correctness verification. Two sync strategies, two execution engines cross-validated bitwise, 600-run chaos sweep.
Distributed training from scratch: ring all-reduce built from send/recv, data/tensor/pipeline parallelism, and honest scaling efficiency measurements
Distributed training from first principles: ring, tree and halving-doubling all-reduce over raw TCP, with data-parallel, fully-sharded, tensor and pipeline parallelism composed into 2D and 3D meshes
Ring-allreduce built from scratch in C++20 over raw MPI point-to-point primitives, benchmarked against MPI_Allreduce across 25 message sizes and 15 process counts, with a fitted alpha-beta cost model explaining the measured bandwidth gap. Includes a reproducible LaTeX report and an engineering postmortem.
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