Facebook

About: Facebook is a company organization based out in Tel Aviv, Israel. It is known for research contribution in the topics: Artificial neural network & Language model. The organization has 7856 authors who have published 10906 publications receiving 570123 citations. The organization is also known as: facebook.com & FB.

CoScale: Coordinating CPU and Memory System DVFS in Server Systems

Abstract: Recent work has introduced memory system dynamic voltage and frequency scaling (DVFS), and has suggested that balanced scaling of both CPU and the memory system is the most promising approach for conserving energy in server systems. In this paper, we first demonstrate that CPU and memory system DVFS often conflict when performed independently by separate controllers. In response, we propose Co Scale, the first method for effectively coordinating these mechanisms under performance constraints. Co Scale relies on execution profiling of each core via (existing and new) performance counters, and models of core and memory performance and power consumption. Co Scale explores the set of possible frequency settings in such a way that it efficiently minimizes the full-system energy consumption within the performance bound. Our results demonstrate that, by effectively coordinating CPU and memory power management, Co Scale conserves a significant amount of system energy compared to existing approaches, while consistently remaining within the prescribed performance bounds. The results also show that Co Scale conserves almost as much system energy as an offline, idealized approach.

SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning.

Abstract: Few-shot learners aim to recognize new object classes based on a small number of labeled training examples. To prevent overfitting, state-of-the-art few-shot learners use meta-learning on convolutional-network features and perform classification using a nearest-neighbor classifier. This paper studies the accuracy of nearest-neighbor baselines without meta-learning. Surprisingly, we find simple feature transformations suffice to obtain competitive few-shot learning accuracies. For example, we find that a nearest-neighbor classifier used in combination with mean-subtraction and L2-normalization outperforms prior results in three out of five settings on the miniImageNet dataset.

Energy efficiency for large-scale MapReduce workloads with significant interactive analysis

Abstract: MapReduce workloads have evolved to include increasing amounts of time-sensitive, interactive data analysis; we refer to such workloads as MapReduce with Interactive Analysis (MIA). Such workloads run on large clusters, whose size and cost make energy efficiency a critical concern. Prior works on MapReduce energy efficiency have not yet considered this workload class. Increasing hardware utilization helps improve efficiency, but is challenging to achieve for MIA workloads. These concerns lead us to develop BEEMR (Berkeley Energy Efficient MapReduce), an energy efficient MapReduce workload manager motivated by empirical analysis of real-life MIA traces at Facebook. The key insight is that although MIA clusters host huge data volumes, the interactive jobs operate on a small fraction of the data, and thus can be served by a small pool of dedicated machines; the less time-sensitive jobs can run on the rest of the cluster in a batch fashion. BEEMR achieves 40-50% energy savings under tight design constraints, and represents a first step towards improving energy efficiency for an increasingly important class of datacenter workloads.

Detecting Social Graph Elements for Structured Search Queries

Abstract: In particular embodiments, a method includes receiving an unstructured text query, parsing the text query to identify n-grams; determining a score that the n-grams correspond to particular nodes and edges from a social graph, identifying those nodes and edges with a score greater than a threshold score, and then generating structured queries that include references to the identified nodes and edges.

Filtering Content in a Social Networking Service

Abstract: A social networking service presents information about the social network using multiple feeds in a user interface and provides mechanisms for filtering the content A content feed includes the most recent content generated by the user's connections, and a highlights feed displays content based on importance and relevance A user may add content to the social networking service through a composer interface A user may also filter either or both of the content feed and the highlights feed using a filtering interface, which allows selective filtering of the feeds using one or more different types of filters, including as filtering by the source of the content, friends or networks, and/or content type