Arpan Jain

Nawras Alnaasan, Arpan Jain, Aamir Shafi et al.

Python has become a dominant programming language for emerging areas like Machine Learning (ML), Deep Learning (DL), and Data Science (DS). An attractive feature of Python is that it provides easy-to-use programming interface while allowing library developers to enhance performance of their applications by harnessing the computing power offered by High Performance Computing (HPC) platforms. Efficient communication is key to scaling applications on parallel systems, which is typically enabled by the Message Passing Interface (MPI) standard and compliant libraries on HPC hardware. mpi4py is a Python-based communication library that provides an MPI-like interface for Python applications allowing application developers to utilize parallel processing elements including GPUs. However, there is currently no benchmark suite to evaluate communication performance of mpi4py -- and Python MPI codes in general -- on modern HPC systems. In order to bridge this gap, we propose OMB-Py -- Python extensions to the open-source OSU Micro-Benchmark (OMB) suite -- aimed to evaluate communication performance of MPI-based parallel applications in Python. To the best of our knowledge, OMB-Py is the first communication benchmark suite for parallel Python applications. OMB-Py consists of a variety of point-to-point and collective communication benchmark tests that are implemented for a range of popular Python libraries including NumPy, CuPy, Numba, and PyCUDA. Our evaluation reveals that mpi4py introduces a small overhead when compared to native MPI libraries. We plan to publicly release OMB-Py to benefit the Python HPC community.

Ammar Ahmad Awan, Arpan Jain, Quentin Anthony et al.

To reduce training time of large-scale DNNs, scientists have started to explore parallelization strategies like data-parallelism, model-parallelism, and hybrid-parallelism. While data-parallelism has been extensively studied and developed, several problems exist in realizing model-parallelism and hybrid-parallelism efficiently. Four major problems we focus on are: 1) defining a notion of a distributed model across processes, 2) implementing forward/back-propagation across process boundaries that requires explicit communication, 3) obtaining parallel speedup on an inherently sequential task, and 4) achieving scalability without losing out on a model's accuracy. To address these problems, we create HyPar-Flow --- a model-size/-type agnostic, scalable, practical, and user-transparent system for hybrid-parallel training by exploiting MPI, Keras, and TensorFlow. HyPar-Flow provides a single API that can be used to perform data, model, and hybrid parallel training of any Keras model at scale. We create an internal distributed representation of the user-provided Keras model, utilize TF's Eager execution features for distributed forward/back-propagation across processes, exploit pipelining to improve performance and leverage efficient MPI primitives for scalable communication. Between model partitions, we use send and recv to exchange layer-data/partial-errors while allreduce is used to accumulate/average gradients across model replicas. Beyond the design and implementation of HyPar-Flow, we also provide comprehensive correctness and performance results on three state-of-the-art HPC systems including TACC Frontera (#5 on Top500.org). For ResNet-1001, an ultra-deep model, HyPar-Flow provides: 1) Up to 1.6x speedup over Horovod-based data-parallel training, 2) 110x speedup over single-node on 128 Stampede2 nodes, and 3) 481x speedup over single-node on 512 Frontera nodes.