Horizontally Fused Training Array: An Effective Hardware Utilization Squeezer for Training Novel Deep Learning Models

Shang Wang, Peiming Yang, Yuxuan Zheng, Xin Li, Gennady Pekhimenko

Driven by the tremendous effort in researching novel deep learning (DL) algorithms, the training cost of developing new models increases staggeringly in recent years. To reduce this training cost and optimize the cluster-wide hardware resource usage, we analyze GPU cluster usage statistics from a well-known research institute. Our study reveals that single-accelerator training jobs can dominate the cluster-wide resource consumption when launched repetitively (e.g., for hyper-parameter tuning) while severely underutilizing the hardware. This is because DL researchers and practitioners often lack the required expertise to independently optimize their own workloads. Fortunately, we observe that such workloads have the following unique characteristics: (i) the models among jobs often have the same types of operators with the same shapes, and (ii) the inter-model horizontal fusion of such operators is mathematically equivalent to other already well-optimized operators. Thus, to help DL researchers and practitioners effectively and easily improve the hardware utilization of their novel DL training workloads, we propose Horizontally Fused Training Array (HFTA). HFTA is a new DL framework extension library that horizontally fuses the models from different repetitive jobs deeply down to operators, and then trains those models simultaneously on a shared accelerator. On three emerging DL training workloads and state-of-the-art accelerators (GPUs and TPUs), HFTA demonstrates strong effectiveness in squeezing out hardware utilization and achieves up to $15.1 \times$ higher training throughput vs. the standard practice of running each job on a separate accelerator.

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