On the Implementation of GNU Prolog

GNU Prolog is a general-purpose implementation of the Prolog language, which distinguishes itself from most other systems by being, above all else, a native-code compiler which produces standalone executables which don't rely on any byte-code emulator or meta-interpreter. Other aspects which stand out include the explicit organization of the Prolog system as a multipass compiler, where intermediate representations are materialized, in Unix compiler tradition. GNU Prolog also includes an extensible and high-performance finite domain constraint solver, integrated with the Prolog language but implemented using independent lower-level mechanisms. This article discusses the main issues involved in designing and implementing GNU Prolog: requirements, system organization, performance and portability issues as well as its position with respect to other Prolog system implementations and the ISO standardization initiative.Comment: 30 pages, 3 figures, To appear in Theory and Practice of Logic Programming (TPLP); Keywords: Prolog, logic programming system, GNU, ISO, WAM, native code compilation, Finite Domain constraint

Compiling Unit Clauses for the Warren Abstract Machine

This thesis describes the design, development, and installation of a computer program which compiles unit clauses generated in a Prolog-based environment at Argonne National Laboratories into Warren Abstract Machine (WAM) code. The program enhances the capabilities of the environment by providing rapid unification and subsumption tests for the very significant class of unit clauses. This should improve performance substantially for large programs that generate and use many unit clauses

The Design and Implementation of a High-Speed Incremental Portable Prolog Compiler

The design and implementation of a relatively portable Prolog compiler achieving 12K LIPS on the standard benchmark is described. The compiler is incremental and uses decompilation to implement retract, clause, and listing, as well as support the needs of its four-port debugger. The system supports modules, garbage collection, database pointers, and a full range of built-ins

Compiling Prolog to Logic-inference Virtual Machine

The Logic-inference Virtual Machine (LVM) is a new Prolog execution model consisting of a set of high-level instructions and memory architecture for handling control and unification. Different from the well-known Warren's Abstract Machine [1], which uses Structure Copying method, the LVM adopts a hybrid of Program Sharing [2] and Structure Copying to represent first-order terms. In addition, the LVM employs a single stack paradigm for dynamic memory allocation and embeds a very efficient garbage collection algorithm to reclaim the useless memory cells. In order to construct a complete Prolog system based on the LVM, a corresponding compiler must be written. In this thesis, a design of such LVM compiler is presented and all important components of the compiler are described. The LVM compiler is developed to translate Prolog programs into LVM bytecode instructions, so that a Prolog program is compiled once and can run anywhere. The first version of LVM compiler (about 8000 lines of C code) has been developed. The compilation time is approximately proportional to the size of source codes. About 80 percent of the time are spent on the global analysis. Some compiled programs have been tested under a LVM emulator. Benchmarks show that the LVM system is very promising in memory utilization and performance

Reducing the cost of South Australia of achieving agreed salinity targets in the River Murray.

Past irrigation development has lead to rising salt loads in the River Murray and its floodplains, and reduced river flows. Even in the absence of any further development, river and floodplain salt loading as the result of this irrigation is anticipated to grow over the decades. Any new development will bring additional salinity loads and further reduced River flows.Australia;river;salinity

Data-parallel concurrent constraint programming.

by Bo-ming Tong.Thesis (M.Phil.)--Chinese University of Hong Kong, 1994.Includes bibliographical references (leaves 104-[110]).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Concurrent Constraint Programming --- p.2Chapter 1.2 --- Finite Domain Constraints --- p.3Chapter 2 --- The Firebird Language --- p.5Chapter 2.1 --- Finite Domain Constraints --- p.6Chapter 2.2 --- The Firebird Computation Model --- p.6Chapter 2.3 --- Miscellaneous Features --- p.7Chapter 2.4 --- Clause-Based N on determinism --- p.9Chapter 2.5 --- Programming Examples --- p.10Chapter 2.5.1 --- Magic Series --- p.10Chapter 2.5.2 --- Weak Queens --- p.14Chapter 3 --- Operational Semantics --- p.15Chapter 3.1 --- The Firebird Computation Model --- p.16Chapter 3.2 --- The Firebird Commit Law --- p.17Chapter 3.3 --- Derivation --- p.17Chapter 3.4 --- Correctness of Firebird Computation Model --- p.18Chapter 4 --- Exploitation of Data-Parallelism in Firebird --- p.24Chapter 4.1 --- An Illustrative Example --- p.25Chapter 4.2 --- Mapping Partitions to Processor Elements --- p.26Chapter 4.3 --- Masks --- p.27Chapter 4.4 --- Control Strategy --- p.27Chapter 4.4.1 --- A Control Strategy Suitable for Linear Equations --- p.28Chapter 5 --- Data-Parallel Abstract Machine --- p.30Chapter 5.1 --- Basic DPAM --- p.31Chapter 5.1.1 --- Hardware Requirements --- p.31Chapter 5.1.2 --- Procedure Calling Convention And Process Creation --- p.32Chapter 5.1.3 --- Memory Model --- p.34Chapter 5.1.4 --- Registers --- p.41Chapter 5.1.5 --- Process Management --- p.41Chapter 5.1.6 --- Unification --- p.49Chapter 5.1.7 --- Variable Table --- p.49Chapter 5.2 --- DPAM with Backtracking --- p.50Chapter 5.2.1 --- Choice Point --- p.52Chapter 5.2.2 --- Trailing --- p.52Chapter 5.2.3 --- Recovering the Process Queues --- p.57Chapter 6 --- Implementation --- p.58Chapter 6.1 --- The DECmpp Massively Parallel Computer --- p.58Chapter 6.2 --- Implementation Overview --- p.59Chapter 6.3 --- Constraints --- p.60Chapter 6.3.1 --- Breaking Down Equality Constraints --- p.61Chapter 6.3.2 --- Processing the Constraint 'As Is' --- p.62Chapter 6.4 --- The Wide-Tag Architecture --- p.63Chapter 6.5 --- Register Window --- p.64Chapter 6.6 --- Dereferencing --- p.65Chapter 6.7 --- Output --- p.66Chapter 6.7.1 --- Collecting the Solutions --- p.66Chapter 6.7.2 --- Decoding the solution --- p.68Chapter 7 --- Performance --- p.69Chapter 7.1 --- Uniprocessor Performance --- p.71Chapter 7.2 --- Solitary Mode --- p.73Chapter 7.3 --- Bit Vectors of Domain Variables --- p.75Chapter 7.4 --- Heap Consumption of the Heap Frame Scheme --- p.77Chapter 7.5 --- Eager Nondeterministic Derivation vs Lazy Nondeterministic Deriva- tion --- p.78Chapter 7.6 --- Priority Scheduling --- p.79Chapter 7.7 --- Execution Profile --- p.80Chapter 7.8 --- Effect of the Number of Processor Elements on Performance --- p.82Chapter 7.9 --- Change of the Degree of Parallelism During Execution --- p.84Chapter 8 --- Related Work --- p.88Chapter 8.1 --- Vectorization of Prolog --- p.89Chapter 8.2 --- Parallel Clause Matching --- p.90Chapter 8.3 --- Parallel Interpreter --- p.90Chapter 8.4 --- Bounded Quantifications --- p.91Chapter 8.5 --- SIMD MultiLog --- p.91Chapter 9 --- Conclusion --- p.93Chapter 9.1 --- Limitations --- p.94Chapter 9.1.1 --- Data-Parallel Firebird is Specialized --- p.94Chapter 9.1.2 --- Limitations of the Implementation Scheme --- p.95Chapter 9.2 --- Future Work --- p.95Chapter 9.2.1 --- Extending Firebird --- p.95Chapter 9.2.2 --- Improvements Specific to DECmpp --- p.99Chapter 9.2.3 --- Labeling --- p.100Chapter 9.2.4 --- Parallel Domain Consistency --- p.101Chapter 9.2.5 --- Branch and Bound Algorithm --- p.102Chapter 9.2.6 --- Other Possible Future Work --- p.102Bibliography --- p.10