Learn More
Mach is a multiprocessor operating system kernel and environment under development at Carnegie Mellon University. Mach provides a new foundation for UNIX development that spans networks of uniprocessors and multiprocessors. This paper describes Mach and the motivations that led to its design. Also described are some of the details of its implementation and(More)
Indigenous emerging economy (EE) firms are increasingly competing in global markets or against multinational corporations (MNCs) in their home markets. But their institutional context at the national and local levels often suffers from what has been termed " institutional weakness " which is believed to put them at a competitive disadvantage on the global(More)
This paper describes the design and implementation of virtual memory management within the CMU Mach Operating System and the experiences gained by the Mach kernel group in porting that system to a variety of architectures. As of this writing, Mach runs on more than half a dozen uniprocessors and multiprocessors including the VAX family of uniprocessors and(More)
The basis of science is the hypothetico-deductive method and the recording of experiments in sufficient detail to enable reproducibility. We report the development of Robot Scientist "Adam," which advances the automation of both. Adam has autonomously generated functional genomics hypotheses about the yeast Saccharomyces cerevisiae and experimentally tested(More)
Mach is a multiprocessor operating system being implemented at Carnegie-Mellon University. An important component of the Mach design is the use of memory objects which can be managed either by the kernel or by user programs through a message interface. This feature allows applications such as transaction management systems to participate in decisions(More)
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(More)
This report describes the design, implementation and performance evaluation of a virtual shared memory server for the Mach operating system. The server provides unrestricted sharing of read-write memory between tasks running on either strongly coupled or loosely coupled architectures, and any mixture thereof. A number of memory coherency algorithms have(More)
Machine learning offers a fantastically powerful toolkit for building complex systems quickly. This paper argues that it is dangerous to think of these quick wins as coming for free. Using the framework of technical debt, we note that it is remarkably easy to incur massive ongoing maintenance costs at the system level when applying machine learning. The(More)
Two subsets of vertices in a graph are called homometric if the multi-sets of distances determined by them are the same. Let h(n) denote the largest number h such that any connected graph of n vertices contains two disjoint homometric subsets of size h. It is shown that c log n log log n < h(n) < n 4 , for n > 3.
We reduce the memory footprint of popular large-scale online learning methods by projecting our weight vector onto a coarse discrete set using randomized rounding. Compared to standard 32-bit float encodings, this reduces RAM usage by more than 50% during training and by up to 95% when making predictions from a fixed model, with almost no loss in accuracy.(More)