Optimizing Distributed Actor Systems for Dynamic Interactive Services - Newell et al. 2016 I'm sure many of you have heard of the Orleans distributed actor system, that was used to build some of the systems supporting Microsoft's online Halo game. Halo Presence is an interactive application which implements presence services for a multi-player game running … Continue reading Optimizing Distributed Actor Systems for Dynamic Interactive Services
Month: April 2016
Data Tiering in Heterogeneous Memory Systems
Data Tiering in Heterogeneous Memory Systems - Dulloor et al. 2016 Another fantastic EuroSys 2016 paper for today, and one with results that are of great importance in understanding the cost and performance implications of the new generation of non-volatile memory (NVM) heading to our data centers soon. Furthermore, we also get some great insight … Continue reading Data Tiering in Heterogeneous Memory Systems
GeePS: Scalable deep learning on distributed GPUs with a GPU-specialized parameter server
GeePS: Scalable deep learning on distributed GPUs with a GPU-specialized parameter server - Cui et al. 2016 (EuroSys 2016) We know that deep learning is well suited to GPUs since it has inherent parallelism. But so far this has mostly been limited to either a single GPU (e.g. using Caffe) or to specially built distributed … Continue reading GeePS: Scalable deep learning on distributed GPUs with a GPU-specialized parameter server
The Linux Scheduler: a Decade of Wasted Cores
The Linux Scheduler: a Decade of Wasted Cores - Lozi et al. 2016 This is the first in a series of papers from EuroSys 2016. There are three strands here: first of all, there's some great background into how scheduling works in the Linux kernel; secondly, there's a story about Software Aging and how changing … Continue reading The Linux Scheduler: a Decade of Wasted Cores
Delta State Replicated Data Types
Delta State Replicated Data Types - Almeida et al. 2016 You know when you want to use CRDTs for their convergence properties, but the amount of state you're required to pass around gets out of hand? In this paper, Almeida et al. show how to retain the advantages of state-based CRDTs, but with much smaller … Continue reading Delta State Replicated Data Types
GloVe: Global Vectors for Word Representation
GloVe: Global Vectors for Word Representation - Pennington et al. 2014 Yesterday we looked at some of the amazing properties of word vectors with word2vec. Pennington et al. argue that the online scanning approach used by word2vec is suboptimal since it doesn't fully exploit statistical information regarding word co-occurrences. They demonstrate a Global Vectors (GloVe) … Continue reading GloVe: Global Vectors for Word Representation
The amazing power of word vectors
For today's post, I've drawn material not just from one paper, but from five! The subject matter is 'word2vec' - the work of Mikolov et al. at Google on efficient vector representations of words (and what you can do with them). The papers are: Efficient Estimation of Word Representations in Vector Space - Mikolov et … Continue reading The amazing power of word vectors
ImageNet Classification with Deep Convolutional Neural Networks
ImageNet Classification with Deep Convolutional Neural Networks - Krizhevsky et al. 2012 Like the large-vocabulary speech recognition paper we looked at yesterday, today's paper has also been described as a landmark paper in the history of deep learning. It's also a surprisingly easy read! The ImageNet dataset contains over 15 million labeled high-resolution images of … Continue reading ImageNet Classification with Deep Convolutional Neural Networks
Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition
Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition - Dahl et al. 2011 The title may be a bit of a mouthful, but this paper is often cited as a watershed moment for deep learning and speech recognition. It represents the first application of deep neural networks for large vocabulary speech recognition (LVSR), and … Continue reading Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition
Deep Learning in Neural Networks: An Overview
Deep Learning in Neural Networks: An Overview - Schmidhuber 2014 What a wonderful treasure trove this paper is! Schmidhuber provides all the background you need to gain an overview of deep learning (as of 2014) and how we got there through the preceding decades. Starting from recent DL results, I tried to trace back the … Continue reading Deep Learning in Neural Networks: An Overview
the morning paper 