Today, predictive digital ad targeting typically relies on detailed user profiles. As consumers deserve and expect more internet privacy, we aim to develop methods to effectively target advertising in a way that respects consumers’ wishes. In particular, we have developed ID-Free targeting, a method to target advertising based on predicted user behavior, without reference to any profile, web browsing history, or any other identifier of the user being targeted. With the restriction that historical user behavior is not available, the only accessible view of the user is the information about the digital ad opportunity itself, such as the URL and the time of day. In this work, we focus on the problem of making brand-specific targeting decisions based on the URL where the ad will be shown, that is, predicting which ad inventories are most likely to lead to a conversion for a particular brand. In order to address this problem, we learn rich URL embedding features for ad inventory URLs. We share the design of our online learning algorithm, which is a practical implementation for training the URL embeddings with the large, dynamic "corpus" that is the internet. We show that this approach to feature representation gives clear performance improvements over a naive representation, and improvements are even more pronounced when brand-specific training data are scarce. The resulting model enables a fully ID-Free delivery of precise ad targeting to users with completely unknown identity and historical behavior.

A growing proportion of digital advertising slots is purchased through real time bidding auctions, which enables advertisers to impose highly specific criteria on which devices and opportunities to target. Employing sophisticated targeting criteria reliably increases the performance of an ad campaign, however too strict criteria will limit its scale. This raises the need to estimate the number of anticipated ad impressions at a given campaign performance level, thus enabling advertisers to tune the campaign's budget to an optimal performance-scale trade-off. In this paper, we provide a way to estimate campaign impressions given the campaign criteria. There are several challenges to this problem. First, the criteria contain logic to include and exclude combinations of audience segments, making the space of possible criteria exponentially large. Furthermore, it is difficult to validate predictions, because we wish to predict the number of impressions available without budget constraints, a situation we can rarely observe in practice. In our approach, we first treat the audience segment inclusion/exclusion criteria separately as a data compression problem, where we use MinHash "sketches" to estimate audience size. We then model the number of available impressions with a regularized linear regression in log space, using multiplier features motivated by the assumption that some components of the additional campaign criteria are conditionally independent. We construct a validation set by projecting observed RTB data (under real budget constraints) to get impression availability without budget constraints. Using this approach, our average prediction is a factor of 2.2 from the true impression availability, and the deployed product responds to user requests in well under a second, meeting both accuracy and latency requirements for decision making in the execution of advertising campaigns.

This paper discusses the design and implementation of a one-sided communication interface for the IBM Blue Gene/L supercomputer. This interface facilitates ARMCI and the Global Arrays toolkit and can be used by other one-sided communication libraries. New protocols, interrupt driven communication, and compute node kernel enhancements were required to enable these libraries. Three possible methods for enabling ARMCI on the Blue Gene/L software stack are discussed. A detailed look into the development process shows how the implementation of the one-sided communication interface was completed. This was accomplished on a compressed time scale with the collaboration of various organizations within IBM and open source communities. In addition to enabling the one-sided libraries, bandwidth enhancements were made for communication along a diagonal on the Blue Gene/L torus network. The maximum bandwidth improved by a factor of three. This work will enable a variety of one-sided applications to run on Blue Gene/L.

Blue Gene/L is currently the world's fastest and most scalable supercomputer. It has demonstrated essentially linear scaling all the way to 131,072 processors in several benchmarks and real applications. The operating systems for the compute and I/O nodes of Blue Gene/L, are among the components responsible for that scalability. Compute nodes are dedicated to running application processes, whereas I/O nodes are dedicated to performing system functions. The operating systems adopted for each of these nodes reflect this separation of function. Compute nodes run a lightweight operating system called the compute node kernel. I/O nodes run a port of the Linux operating system. This paper discusses the architecture and design of this solution for Blue Gene/L in the context of the hardware characteristics that led to the design decisions. It also explains and demonstrates how those decisions are instrumental in achieving the performance and scalability for which Blue Gene/L is famous.

This paper introduces a new broadcast query (BQ) forwarding technique for multipath routing protocols that operate within wireless mobile ad-hoc networks (MANET). This technique increases the number of node disjoint routes created by up to 75% over standard broadcast forwarding strategies without causing any increase in the number of BQs forwarded.