Instrumentation with cement-augmented pedicle screws has expanded the therapeutic spectrum. This technique is useful for the palliation of bone metastases and in generalized osteoporosis. Serious complications such as pulmonary embolism have been described following percutaneous vertebroplasty, a frequently used technique. We report the case of a 55-year-old patient with a large central Palacos embolism of the right pulmonary artery after corporectomy of the lumbar vertebrae 3 and 4 and reconstruction using autologous pelvic bone. The large Palacos embolism was removed successfully from the right pulmonary artery with extracorporeal circulation.

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Summary We describe the Auto‐Tuning Framework (ATF) — a simple‐to‐use, generic approach and its implementation, as a framework for automatic program optimization by choosing the most suitable values of program parameters such as the number of parallel threads, tile sizes, etc. ATF combines four major advantages over the state‐of‐the‐art auto‐tuning: i) it is generic regarding the programming language, application domain, tuning objective (eg, high performance and/or low energy consumption), and search technique; ii) it can auto‐tune a broader class of applications by allowing tuning parameters to be interdependent, eg, when one parameter is divisible by another parameter; iii) it allows tuning parameters to have substantially larger ranges by implementing an optimized search space generation process; and iv) it is arguably simpler to use, eg, the ATF user prepares an application for auto‐tuning by annotating its source code with simple tuning directives. We demonstrate ATF's efficacy by comparing it to the state‐of‐the‐art auto‐tuning approaches, OpenTuner and CLTune; ATF shows better tuning results with less programmer's effort.

Auto-tuning is a popular approach to program optimization: it automatically finds good configurations of a program’s so-called tuning parameters whose values are crucial for achieving high performance for a particular parallel architecture and characteristics of input/output data. We present three new contributions of the Auto-Tuning Framework (ATF), which enable a key advantage in general-purpose auto-tuning : efficiently optimizing programs whose tuning parameters have interdependencies among them. We make the following contributions to the three main phases of general-purpose auto-tuning: (1) ATF generates the search space of interdependent tuning parameters with high performance by efficiently exploiting parameter constraints; (2) ATF stores such search spaces efficiently in memory, based on a novel chain-of-trees search space structure; (3) ATF explores these search spaces faster, by employing a multi-dimensional search strategy on its chain-of-trees search space representation. Our experiments demonstrate that, compared to the state-of-the-art, general-purpose auto-tuning frameworks, ATF substantially improves generating, storing, and exploring the search space of interdependent tuning parameters, thereby enabling an efficient overall auto-tuning process for important applications from popular domains, including stencil computations, linear algebra routines, quantum chemistry computations, and data mining algorithms.

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