Hidden Semi-Markov Models (HSMMs) are fundamental probabilistic models widely adopted across diverse domains, from computational biology to finance and signal processing. The Viterbi algorithm decodes the most likely state sequence given an HSMM and can be applied iteratively for ab initio model learning. However, existing Viterbi implementations remain sequential, and GPU-accelerated solutions are entirely absent, making HSMM decoding impractical for large-scale workloads. We present a tensor-based formulation of the Viterbi algorithm for HSMMs, restructuring the inner loops into tensor operations that naturally map onto SIMD units and massively parallel architectures. Building on this formulation, we provide optimized implementations spanning single- and multi-core CPUs, and, for the first time, GPU. Experimental evaluation demonstrates speedups of up to 14x on a single core, over 200x with multi-core, and over 570x on GPU over the state-of-the-art sequential baseline, establishing a new performance baseline for large-scale HSMM decoding.
Lorenzo Piarulli, E. Belli, D. De Sensi· 0 citations
Characterising AI workload performance on modern HPC systems requires understanding both their scalability in isolation and their behaviour under concurrent execution. However, the interplay among parallelisation strategies, network congestion, compute capability, and interconnect technologies remains poorly understood. This work investigates the performance and scalability of AI models up to 2400 GPUs. We quantify the communication overheads and their impact across different interconnects by evaluating scale-up, scale-out, and rack-scale configurations under multiple allocation schemes. Finally, we study how multiple concurrent training jobs interfere with each other by designing a realistic noise model. We design a benchmark suite of AI models to evaluate the performance of five distinct parallelisation strategies across different supercomputing clusters, including Alps, Leonardo, LUMI, JUPITER, NVL72 GB300, and DGX A100. Our work provides a systematic characterization of the scalability and execution efficiency of distributed AI training, while offering key insights into performance behavior under realistic multi-tenant scenarios.
Jacopo Raffi, Thomas Pasquali, L. Piarulli et al.· 0 citations
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