Work done at Embedded LLM on GPU kernel acceleration for biological and medical foundation models, benchmarked cross-vendor on NVIDIA H100 and AMD MI300X. Each entry has a dedicated page with its key findings.
A vendor-portable fused-operator protocol runs clinical LMs and VLMs on-premises so PHI never leaves the hospital: 1.64× faster encoding at half the energy per slide, 2.03× slides cleared per shift, unchanged across NVIDIA H100 and AMD MI300X with ΔAUC ≤ 0.001.
A fused GPU kernel folds irregular-sampling interpolation and SSM inference into a single launch, cutting end-to-end latency 35.7× and clearing the sub-50ms bedside target while improving AUROC over GRU-D.
Profiling-driven kernel fusion folds 3–8 TensorNet operations into single GPU launches, cutting kernel launches by 67–88% for a 2.82× end-to-end speedup that turns a 13-hour MD run into 4.6 hours, with physical accuracy preserved exactly.
A measurement study across six bio models and three GPUs: 60–80% of runtime sits outside dense compute, vendor-stack asymmetry turns a trivial bug into a 38.8× swing, and one Triton source closes the gap on both NVIDIA and AMD.
One portable Triton fusion framework accelerates six biological models and algorithms (DualBind, AlphaGenome, Enformer, ESM-2, ProtBERT, Needleman-Wunsch) up to 720× across NVIDIA and AMD GPUs, with zero accuracy loss.
Per-model drop-in Triton kernels accelerate protein and genomic foundation models plus classical algorithms (Smith-Waterman, Needleman-Wunsch, k-mer indexing, BWT) up to 2,986× across NVIDIA and AMD, with correctness preserved to machine precision.
A library of 20+ write-once Triton kernels delivering 10–19,000× speedups for sequence alignment, k-mer indexing, and quality control, compiling through MLIR to run identically on NVIDIA and AMD with no source changes.
An accepted talk on the fused-kernel ICU work, presenting the systems story behind sub-50ms bedside inference: the 85% preprocessing bottleneck, the single-launch fix, and the latency, throughput, and accuracy gains across NVIDIA and AMD.