An Evaluation of TensorFlow as a Programming Framework for HPC Applications

Abstract: In recent years, deep-learning, a branch of machine learning gained increasing popularity due to their extensive applications and performance. At the core of these application is dense matrix-matrix multiplication. Graphics Processing Units (GPUs) are commonly used in the training process due to their massively parallel computation capabilities. In addition, specialized low-precision accelerators have emerged to specifically address Tensor operations. Software frameworks, such as TensorFlow have also emerged to increase the expressiveness of neural network model development. In TensorFlow computation problems are expressed as Computation Graphs where nodes of a graph denote operation and edges denote data movement between operations. With increasing number of heterogeneous accelerators which might co-exist on the same cluster system, it became increasingly difficult for users to program efficient and scalable applications. TensorFlow provides a high level of abstraction and it is possible to place operations of a computation graph on a device easily through a high level API. In this work, the usability of TensorFlow as a programming framework for HPC application is reviewed. We give an introduction of TensorFlow as a programming framework and paradigm for distributed computation. Two sample applications are implemented on TensorFlow: tiled matrix multiplication and conjugate gradient solver for solving large linear systems. We try to illustrate how such problems can be expressed in computation graph for distributed computation. We perform scalability tests and comment on performance scaling results and quantify how TensorFlow can take advantage of HPC systems by performing micro-benchmarking on communication performance. Through this work, we show that TensorFlow is an emerging and promising platform which is well suited for a particular class of problem which requires very little synchronization.