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Found 6 papers, 4 papers with code
The Case for Co-Designing Model Architectures with Hardware
While GPUs are responsible for training the vast majority of state-of-the-art deep learning models, the implications of their architecture are often overlooked when designing new deep learning (DL) models.
Exploiting Inter-Layer Expert Affinity for Accelerating Mixture-of-Experts Model Inference
Unlike previous methods, our solution can be directly applied to pre-trained MoE models without any fine-tuning or accuracy degradation.
Flover: A Temporal Fusion Framework for Efficient Autoregressive Model Parallel Inference
Inference on these models, by design, harnesses a temporal dependency, where the current token's probability distribution is conditioned on preceding tokens.
MCR-DL: Mix-and-Match Communication Runtime for Deep Learning
However, such distributed DL parallelism strategies require a varied mixture of collective and point-to-point communication operations across a broad range of message sizes and scales.
OMB-Py: Python Micro-Benchmarks for Evaluating Performance of MPI Libraries on HPC Systems
However, there is currently no benchmark suite to evaluate communication performance of mpi4py -- and Python MPI codes in general -- on modern HPC systems.
Efficient MPI-based Communication for GPU-Accelerated Dask Applications
This paper presents the design and implementation of a new communication backend for Dask -- called MPI4Dask -- that is targeted for modern HPC clusters built with GPUs.
