Predicting ad click-through rates (CTR) is a massive-scale learning problem that is central to the multi-billion dollar online advertising industry. We present a selection of case studies and topics drawn from recent experiments in the setting of a deployed CTR prediction system. These include improvements in the context of traditional supervised learning based on an FTRL-Proximal online learning algorithm (which has excellent sparsity and convergence properties) and the use of per-coordinate learning rates.We also explore some of the challenges that arise in a real-world system that may appear at first to be outside the domain of traditional machine learning research. These include useful tricks for memory savings, methods for assessing and visualizing performance, practical methods for providing confidence estimates for predicted probabilities, calibration methods, and methods for automated management of features. Finally, we also detail several directions that did not turn out to be beneficial for us, despite promising results elsewhere in the literature. The goal of this paper is to highlight the close relationship between theoretical advances and practical engineering in this industrial setting, and to show the depth of challenges that appear when applying traditional machine learning methods in a complex dynamic system.
Abstract. The crossing number of a graph is the minimum number of edge crossings in any drawing of the graph into the plane. This very basic property has been studied extensively in the literature from a theoretic point of view and many bounds exist for a variety of graph classes. In this paper, we present the first algorithm able to compute the crossing number of general sparse graphs of moderate size and present computational results on a popular benchmark set of graphs. The approach uses a new integer linear programming formulation of the problem combined with strong heuristics and problem reduction techniques. This enables us to compute the crossing number for 91 percent of all graphs on up to 40 nodes in the benchmark set within a time limit of five minutes per graph.
Abstract-High-performance scientific applications are usually built from software modules written in multiple programming languages. This raises the issue of language interoperability which involves making calls between languages, converting basic types, and bridging disparate programming models. Babel provides a featurerich, extensible, high-performance solution to the language interoperability problem currently supporting C, C++, FORTRAN 77, Fortran 90/95, Fortran 2003/2008, Python, and Java. Babel supports object-oriented programming features and interface semantics with runtime-enforcement. In addition to in-process language interoperability, Babel includes remote method invocation to support hybrid parallel and distributed computing paradigms.
Register allocation has gained renewed attention in the recent past. Several authors propose a separation of the problem into decoupled sub-tasks including spilling, allocation, assignment, and coalescing. This approach is largely motivated by recent advances in SSA-based register allocation that suggest that a decomposition does not significantly degrade the overall allocation quality.The algorithmic challenges of intra-procedural spilling have been neglected so far and very crude heuristics were employed. In this work, (1) we introduce the constrained mincut (CMC) problem for solving the spilling problem, (2) we provide an integer linear program formulation for computing an optimal solution of CMC, and (3) we devise a progressive Lagrangian solver that is viable for production compilers. Our experiments with Spec2k and MiBench show that optimal solutions are feasible, even for very large programs, and that heuristics leave significant potential behind for small register files.
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