Macros That Work

9 pagesThis paper describes a modified form of Kohlbecker's algorithm for reliably hygienic (capture-free) macro expansion in block-structured languages, where macros are source-tos-ource transformations specified using a high-level pattern language. Unlike previous algorithms, the modified algorithm runs in linear instead of quadratic time, copies few constants, does not assume that syntactic keywords ( e.g. if) are reserved words, and allows local (scoped) macros to refer to lexical variables in a referentially transparent manner. Syntactic closures have been advanced as an alternative to hygienic macro expansion. The problem with syntactic closures is that they are inherently low-level and therefore difficult to use correctly, especially when syntactic keywords are not reserved. It is impossible to construct a pattern-based, automatically hygienic macro system on top of syntactic closures because the pattern interpreter must be able to determine the syntactic role of an identifier (in order to close it in the correct syntactic environment) before macro expansion has made that role apparent. may be viewed as a book-keeping technique for deferring such decisions until macro expansion is locally complete. Building on that insight, this paper unifies and extends the competing paradigms of hygienic macro expansion and syntactic closures to obtain an algorithm that combines the benefits of both . Several prototypes of a complete macro system for Scheme have been based on the algorithm presented here

On the Online Generation of Effective Macro-operators

Macro-operator (“macro”, for short) generation is a well-known technique that is used to speed-up the planning process. Most published work on using macros in automated planning relies on an offline learning phase where training plans, that is, solutions of simple problems, are used to generate the macros. However, there might not always be a place to accommodate training. In this paper we propose OMA, an efficient method for generating useful macros without an offline learning phase, by utilising lessons learnt from existing macro learning techniques. Empirical evaluation with IPC benchmarks demonstrates performance improvement in a range of state-of-the-art planning engines, and provides insights into what macros can be generated without training

Evolving macro-actions for planning

Domain re-engineering through macro-actions (i.e. macros) provides one potential avenue for research into learning for planning. However, most existing work learns macros that are reusable plan fragments and so observable from planner behaviours online or plan characteristics offline. Also, there are learning methods that learn macros from domain analysis. Nevertheless, most of these methods explore restricted macro spaces and exploit specific features of planners or domains. But, the learning examples, especially that are used to acquire previous experiences, might not cover many aspects of the system, or might not always reflect that better choices have been made during the search. Moreover, any specific properties are not likely to be common with many planners or domains. This paper presents an offline evolutionary method that learns macros for arbitrary planners and domains. Our method explores a wider macro space and learns macros that are somehow not observable from the examples. Our method also represents a generalised macro learning framework as it does not discover or utilise any specific structural properties of planners or domains

DREAMPlaceFPGA-MP: An Open-Source GPU-Accelerated Macro Placer for Modern FPGAs with Cascade Shapes and Region Constraints

FPGA macro placement plays a pivotal role in routability and timing closer to the modern FPGA physical design flow. In modern FPGAs, macros could be subject to complex cascade shape constraints requiring instances to be placed in consecutive sites. In addition, in real-world FPGA macro placement scenarios, designs could have various region constraints that specify boundaries within which certain design instances and macros should be placed. In this work, we present DREAMPlaceFPGA-MP, an open-source GPU-accelerated FPGA macro-placer that efficiently generates legal placements for macros while honoring cascade shape requirements and region constraints. Treating multiple macros in a cascade shape as a large single instance and restricting instances to their respective regions, DREAMPlaceFPGA-MP obtains roughly legal placements. The macros are legalized in multiple steps to efficiently handle cascade shapes and region constraints. Our experimental results demonstrate that DREAMPlaceFPGA-MP is among the top contestants of the MLCAD 2023 FPGA Macro-Placement Contest

Wormholes and Off-Diagonal Solutions in f(R,T), Einstein and Finsler Gravity Theories

The aims of this work are 1) to sketch a proof that there are such parameterizations of the local frame and canonical connection structures when the gravitational field equations in f(R,T)-modified gravity, MG, can be integrated in generic off-diagonal forms with metrics depending on all spacetime coordinates and 2) to provide some examples of exact solutions.Comment: 4 pages, ERE2012-Proceedings macros, Contribution to the Spanish Relativity Meeting in Portugal, Guimaraes, September 3-7, 201

Macro actions for structures

It is not surprising that structures underly many of the problems that we find interesting in planning. However, the planners that we develop are not always capable of acting on them as they increase in size. For example, the errors caused through relaxations in a heuristic can grow quickly when acting on a structure. Macro actions can help to compensate for heuristic error; however, researchers have investigated finite length macro actions limiting the benefit when the underlying problem is an arbitrary sized structure. In this work we design a specific set of arbitrary length macros, providing a vocabulary for acting on structures

ROOT - A C++ Framework for Petabyte Data Storage, Statistical Analysis and Visualization

ROOT is an object-oriented C++ framework conceived in the high-energy physics (HEP) community, designed for storing and analyzing petabytes of data in an efficient way. Any instance of a C++ class can be stored into a ROOT file in a machine-independent compressed binary format. In ROOT the TTree object container is optimized for statistical data analysis over very large data sets by using vertical data storage techniques. These containers can span a large number of files on local disks, the web, or a number of different shared file systems. In order to analyze this data, the user can chose out of a wide set of mathematical and statistical functions, including linear algebra classes, numerical algorithms such as integration and minimization, and various methods for performing regression analysis (fitting). In particular, ROOT offers packages for complex data modeling and fitting, as well as multivariate classification based on machine learning techniques. A central piece in these analysis tools are the histogram classes which provide binning of one- and multi-dimensional data. Results can be saved in high-quality graphical formats like Postscript and PDF or in bitmap formats like JPG or GIF. The result can also be stored into ROOT macros that allow a full recreation and rework of the graphics. Users typically create their analysis macros step by step, making use of the interactive C++ interpreter CINT, while running over small data samples. Once the development is finished, they can run these macros at full compiled speed over large data sets, using on-the-fly compilation, or by creating a stand-alone batch program. Finally, if processing farms are available, the user can reduce the execution time of intrinsically parallel tasks - e.g. data mining in HEP - by using PROOF, which will take care of optimally distributing the work over the available resources in a transparent way

More on Chiral-Nonchiral Dual Pairs

Expanding upon earlier work of Pouliot and Strassler, we construct chiral magnetic duals to nonchiral supersymmetric electric theories based upon SO(7), SO(8) and SO(9) gauge groups with various numbers of vector and spinor matter superfields. Anomalies are matched and gauge invariant operators are mapped within each dual pair. Renormalization group flows along flat directions are also examined. We find that confining phase quantum constraints in the electric theories are recovered from semiclassical equations of motion in their magnetic counterparts when the dual gauge groups are completely Higgsed.Comment: 25 pages, harvmac and tables macros, 1 figur