Model Accuracy and Runtime Tradeoff in Distributed Deep Learning:A Systematic Study

This paper presents Rudra, a parameter server based distributed computing framework tuned for training large-scale deep neural networks. Using variants of the asynchronous stochastic gradient descent algorithm we study the impact of synchronization protocol, stale gradient updates, minibatch size, learning rates, and number of learners on runtime performance and model accuracy. We introduce a new learning rate modulation strategy to counter the effect of stale gradients and propose a new synchronization protocol that can effectively bound the staleness in gradients, improve runtime performance and achieve good model accuracy. Our empirical investigation reveals a principled approach for distributed training of neural networks: the mini-batch size per learner should be reduced as more learners are added to the system to preserve the model accuracy. We validate this approach using commonly-used image classification benchmarks: CIFAR10 and ImageNet.Comment: Accepted by The IEEE International Conference on Data Mining 2016 (ICDM 2016

Thinking Fast and Slow with Deep Learning and Tree Search

Sequential decision making problems, such as structured prediction, robotic control, and game playing, require a combination of planning policies and generalisation of those plans. In this paper, we present Expert Iteration (ExIt), a novel reinforcement learning algorithm which decomposes the problem into separate planning and generalisation tasks. Planning new policies is performed by tree search, while a deep neural network generalises those plans. Subsequently, tree search is improved by using the neural network policy to guide search, increasing the strength of new plans. In contrast, standard deep Reinforcement Learning algorithms rely on a neural network not only to generalise plans, but to discover them too. We show that ExIt outperforms REINFORCE for training a neural network to play the board game Hex, and our final tree search agent, trained tabula rasa, defeats MoHex 1.0, the most recent Olympiad Champion player to be publicly released.Comment: v1 to v2: - Add a value function in MCTS - Some MCTS hyper-parameters changed - Repetition of experiments: improved accuracy and errors shown. (note the reduction in effect size for the tpt/cat experiment) - Results from a longer training run, including changes in expert strength in training - Comparison to MoHex. v3: clarify independence of ExIt and AG0. v4: see appendix

A Parallel Tree-SPH code for Galaxy Formation

We describe a new implementation of a parallel Tree-SPH code with the aim to simulate Galaxy Formation and Evolution. The code has been parallelized using SHMEM, a Cray proprietary library to handle communications between the 256 processors of the Silicon Graphics T3E massively parallel supercomputer hosted by the Cineca Supercomputing Center (Bologna, Italy). The code combines the Smoothed Particle Hydrodynamics (SPH) method to solve hydro-dynamical equations with the popular Barnes and Hut (1986) tree-code to perform gravity calculation with a NlogN scaling, and it is based on the scalar Tree-SPH code developed by Carraro et al(1998)[MNRAS 297, 1021]. Parallelization is achieved distributing particles along processors according to a work-load criterion. Benchmarks, in terms of load-balance and scalability, of the code are analyzed and critically discussed against the adiabatic collapse of an isothermal gas sphere test using 20,000 particles on 8 processors. The code results balanced at more that 95% level. Increasing the number of processors, the load-balance slightly worsens. The deviation from perfect scalability at increasing number of processors is almost negligible up to 32 processors. Finally we present a simulation of the formation of an X-ray galaxy cluster in a flat cold dark matter cosmology, using 200,000 particles and 32 processors, and compare our results with Evrard (1988) P3M-SPH simulations. Additionaly we have incorporated radiative cooling, star formation, feed-back from SNae of type II and Ia, stellar winds and UV flux from massive stars, and an algorithm to follow the chemical enrichment of the inter-stellar medium. Simulations with some of these ingredients are also presented.Comment: 19 pages, 14 figures, accepted for publication in MNRA