Receipt from Leather Craft of Malvern to Robert Goelet

https://digitalcommons.salve.edu/goelet-personal-expenses/1268/thumbnail.jp

On-the-fly memory compression for multibody algorithms.

Memory and bandwidth demands challenge developers of particle-based codes that have to scale on new architectures, as the growth of concurrency outperforms improvements in memory access facilities, as the memory per core tends to stagnate, and as communication networks cannot increase bandwidth arbitrary. We propose to analyse each particle of such a code to find out whether a hierarchical data representation storing data with reduced precision caps the memory demands without exceeding given error bounds. For admissible candidates, we perform this compression and thus reduce the pressure on the memory subsystem, lower the total memory footprint and reduce the data to be exchanged via MPI. Notably, our analysis and transformation changes the data compression dynamically, i.e. the choice of data format follows the solution characteristics, and it does not require us to alter the core simulation code

Проблемы формирования государственной экологической политики в условиях ограниченности ресурсов

Проблема ограниченности ресурсов является основой развития экономики. Однако также ограниченность оказывает влияние и на экологическую политику в стране. В статье рассмотрены ключевые механизмы формирования государственной экологической политики, экологического менеджмента; предложены пути повышения эффективности экологической политики.The problem of limited resources is the basis for economic development. However, the limited nature also influences the environmental policy in the country. The article considers the key mechanisms for the formation of state environmental policy, environmental management; ways to improve the effectiveness of environmental policy

Correlation techniques applied to antenna pattern measurement

A correlation processor based on the excellent periodic autocorrelation properties of maximal-length pseudorandom binary sequences has been used in antenna pattern measurements to resolve the direct (wanted) path from any unwanted multipath components. A simple implementation of the technique has been used to make measurements in a controlled environment; the results show that the multipath effects are almost completely eliminated and an accurate pattern measurement is obtained

Synthesizing benchmarks for predictive modeling

Predictive modeling using machine learning is an effective method for building compiler heuristics, but there is a shortage of benchmarks. Typical machine learning experiments outside of the compilation field train over thousands or millions of examples. In machine learning for compilers, however, there are typically only a few dozen common benchmarks available. This limits the quality of learned models, as they have very sparse training data for what are often high-dimensional feature spaces. What is needed is a way to generate an unbounded number of training programs that finely cover the feature space. At the same time the generated programs must be similar to the types of programs that human developers actually write, otherwise the learning will target the wrong parts of the feature space. We mine open source repositories for program fragments and apply deep learning techniques to automatically construct models for how humans write programs. We sample these models to generate an unbounded number of runnable training programs. The quality of the programs is such that even human developers struggle to distinguish our generated programs from hand-written code. We use our generator for OpenCL programs, CLgen, to automatically synthesize thousands of programs and show that learning over these improves the performance of a state of the art predictive model by 1.27×. In addition, the fine covering of the feature space automatically exposes weaknesses in the feature design which are invisible with the sparse training examples from existing benchmark suites. Correcting these weaknesses further increases performance by 4.30×

Project MOSI: rationale and pilot-study results of an initiative to help protect zoo animals from mosquito-transmitted pathogens and contribute data on mosquito spatio–temporal distribution change

Mosquito-borne pathogens pose major threats to both wildlife and human health and, largely as a result of unintentional human-aided dispersal of their vector species, their cumulative threat is on the rise. Anthropogenic climate change is expected to be an increasingly significant driver of mosquito dispersal and associated disease spread. The potential health implications of changes in the spatio-temporal distribution of mosquitoes highlight the importance of ongoing surveillance and, where necessary, vector control and other health-management measures. The World Association of Zoos and Aquariums initiative, Project MOSI, was established to help protect vulnerable wildlife species in zoological facilities from mosquito-transmitted pathogens by establishing a zoo-based network of fixed mosquito monitoring sites to assist wildlife health management and contribute data on mosquito spatio-temporal distribution changes. A pilot study for Project MOSI is described here, including project rationale and results that confirm the feasibility of conducting basic standardized year-round mosquito trapping and monitoring in a zoo environment

On-the-fly memory compression for multibody algorithms

Memory and bandwidth demands challenge developers of particle-based codes that have to scale on new architectures, as the growth of concurrency outperforms improvements in memory access facilities, as the memory per core tends to stagnate, and as communication networks cannot increase bandwidth arbitrary. We propose to analyse each particle of such a code to find out whether a hierarchical data representation storing data with reduced precision caps the memory demands without exceeding given error bounds. For admissible candidates, we perform this compression and thus reduce the pressure on the memory subsystem, lower the total memory footprint and reduce the data to be exchanged via MPI. Notably, our analysis and transformation changes the data compression dynamically, i.e. the choice of data format follows the solution characteristics, and it does not require us to alter the core simulation code

Function Merging by Sequence Alignment

Resource-constrained devices for embedded systems are becoming increasingly important. In such systems, memory is highly restrictive, making code size in most cases even more important than performance. Compared to more traditional platforms, memory is a larger part of the cost and code occupies much of it. Despite that, compilers make little effort to reduce code size. One key technique attempts to merge the bodies of similar functions. However, production compilers only apply this optimization to identical functions, while research compilers improve on that by merging the few functions with identical control-flow graphs and signatures. Overall, existing solutions are insufficient and we end up having to either increase cost by adding more memory or remove functionality from programs. We introduce a novel technique that can merge arbitrary functions through sequence alignment, a bioinformatics algorithm for identifying regions of similarity between sequences. We combine this technique with an intelligent exploration mechanism to direct the search towards the most promising function pairs. Our approach is more than 2.4x better than the state-of-the-art, reducing code size by up to 25%, with an overall average of 6%, while introducing an average compilation-time overhead of only 15%. When aided by profiling information, this optimization can be deployed without any significant impact on the performance of the generated code

HyFM: Function Merging for Free

Function merging is an important optimization for reducing code size. It merges multiple functions into a single one, eliminating duplicate code among them. The existing state-of-the-art relies on a well-known sequence alignment algorithm to identify duplicate code across whole functions. However, this algorithm is quadratic in time and space on the number of instructions. This leads to very high time overheads and prohibitive levels of memory usage even for medium-sized benchmarks. For larger programs, it becomes impractical. This is made worse by an overly eager merging approach. All selected pairs of functions will be merged. Only then will this approach estimate the potential benefit from merging and decide whether to replace the original functions with the merged one. Given that most pairs are unprofitable, a significant amount of time is wasted producing merged functions that are simply thrown away. In this paper, we propose HyFM, a novel function merging technique that delivers similar levels of code size reduction for significantly lower time overhead and memory usage. Unlike the state-of-the-art, our alignment strategy works at the block level. Since basic blocks are usually much shorter than functions, even a quadratic alignment is acceptable. However, we also propose a linear algorithm for aligning blocks of the same size at a much lower cost. We extend this strategy with a multi-tier profitability analysis that bails out early from unprofitable merging attempts. By aligning individual pairs of blocks, we are able to decide their alignment’s profitability separately and before actually generating code. Experimental results on SPEC 2006 and 2017 show that HyFM needs orders of magnitude less memory, using up to 48 MB or 5.6 MB, depending on the variant used, while the state-of-the-art requires 32 GB in the worst case. HyFM also runs over 4.5×× faster, while still achieving comparable code size reduction. Combined with the speedup of later compilation stages due to the reduced number of functions, HyFM contributes to a reduced end-to-end compilation time