2 research outputs found
Improving the performance of dataflow systems for deep neural network training
Deep neural networks (DNNs) have led to significant advancements in machine learning.
With deep structure and flexible model parameterisation, they exhibit state-of-the-art accuracies for many complex tasks e.g. image recognition. To achieve this, models are trained iteratively over large datasets. This process involves expensive matrix operations, making it time-consuming to obtain converged models. To accelerate training, dataflow systems parallelise computation. A scalable approach is to use parameter server framework: it has workers that train model replicas in parallel and parameter servers that synchronise the replicas to ensure the convergence.
With distributed DNN systems, there are three challenges that determine the training completion time. In this thesis, we propose practical and effective techniques to address each of these challenges.
Since frequent model synchronisation results in high network utilisation, the parameter server approach can suffer from network bottlenecks, thus requiring decisions on resource allocation. Our idea is to use all available network bandwidth and synchronise subject to the available bandwidth. We present Ako, a DNN system that uses partial gradient exchange for synchronising replicas in a peer-to-peer fashion. We show that our technique exhibits a 25% lower convergence time than a hand-tuned parameter-server deployments.
For a long training, the compute efficiency of worker nodes is important. We argue that processing hardware should be fully utilised for the best speed-up. The key observation is it is possible to overlap the execution of several matrix operations with other workloads. We describe Crossbow, a GPU-based system that maximises hardware utilisation. By using a multi-streaming scheduler, multiple models are trained in parallel on GPU and achieve a 2.3x speed-up compared to a state-of-the-art system.
The choice of model configuration for replicas also directly determines convergence quality. Dataflow systems are used for exploring the promising configurations but provide little support for efficient exploratory workflows. We present Meta-dataflow (MDF), a dataflow model that expresses complex workflows. By taking into account all configurations as a unified workflow, MDFs efficiently reduce time spent on configuration exploration.Open Acces
Communication-Efficient Distributed Deep Learning: A Comprehensive Survey
Distributed deep learning becomes very common to reduce the overall training
time by exploiting multiple computing devices (e.g., GPUs/TPUs) as the size of
deep models and data sets increases. However, data communication between
computing devices could be a potential bottleneck to limit the system
scalability. How to address the communication problem in distributed deep
learning is becoming a hot research topic recently. In this paper, we provide a
comprehensive survey of the communication-efficient distributed training
algorithms in both system-level and algorithmic-level optimizations. In the
system-level, we demystify the system design and implementation to reduce the
communication cost. In algorithmic-level, we compare different algorithms with
theoretical convergence bounds and communication complexity. Specifically, we
first propose the taxonomy of data-parallel distributed training algorithms,
which contains four main dimensions: communication synchronization, system
architectures, compression techniques, and parallelism of communication and
computing. Then we discuss the studies in addressing the problems of the four
dimensions to compare the communication cost. We further compare the
convergence rates of different algorithms, which enable us to know how fast the
algorithms can converge to the solution in terms of iterations. According to
the system-level communication cost analysis and theoretical convergence speed
comparison, we provide the readers to understand what algorithms are more
efficient under specific distributed environments and extrapolate potential
directions for further optimizations