相关论文: Solving the TTC 2011 Compiler Optimization Case wi…

This paper discusses the GReTL solution of the TTC 2011 Compiler Optimization case. The submitted solution covers both the constant folding task and the instruction selection task. The verifier for checking the validity of the graph is also…

This report presents a solution to the Hello World case study of TTC 2011 using GROOVE. We provide and explain the grammar that we used to solve the case study. Every requested question of the case study was solved by a single rule…

The challenge of the Compiler Optimization Case is to perform local optimizations and instruction selection on the graph-based intermediate representation of a compiler. The case is designed to compare participating tools regarding their…

This paper describes the GROOVE solution to the "Class Diagram Restructuring" case study of the Tool Transformation Contest 2013. We show that the visual rule formalism enables the required restructuring to be formulated in a very concise…

In this short paper we present our solution for the Compiler Optimization case study of the Transformation Tool Contest (TTC) 2011 using the QVTR-XSLT tool. The tool supports editing and execution of the graphical notation of QVT Relations…

An optimizing compiler consists of a front end parsing a textual programming language into an intermediate representation (IR), a middle end performing optimizations on the IR, and a back end lowering the IR to a target representation (TR)…

SCOOP is a programming model and language that allows concurrent programming at a high level of abstraction. Several approaches to verifying SCOOP programs have been proposed in the past, but none of them operate directly on the source code…

Compiler pass selection and phase ordering present a significant challenge in achieving optimal program performance, particularly for objectives like code size reduction. Standard compiler heuristics offer general applicability but often…

Graphs have been widely used to represent complex data in many applications. Efficient and effective analysis of graphs is important for graph-based applications. However, most graph analysis tasks are combinatorial optimization (CO)…

Graph is a natural representation of data for a variety of real-word applications, such as knowledge graph mining, social network analysis and biological network comparison. For these applications, graph embedding is crucial as it provides…

Combinatorial optimization problems are ubiquitous in science and engineering. Still, learning-based approaches to accelerate combinatorial optimization often require solving a large number of difficult instances to collect training data,…

Most compilers for machine learning (ML) frameworks need to solve many correlated optimization problems to generate efficient machine code. Current ML compilers rely on heuristics based algorithms to solve these optimization problems one at…

Graph Representation Learning (GRL) methods opened new avenues for addressing complex, real-world problems represented by graphs. However, many graphs used in these applications comprise millions of nodes and billions of edges and are…

The authors' "metatools" are a collection of tools for generic programming. This includes generating Java sources from mathematically well-founded specifications, as well as the creation of strictly typed document object models for XML…

This paper discusses the GReTL reference solution of the TTC 2011 Reengineering case. Given a Java syntax graph, a simple state machine model has to be extracted. The submitted solution covers both the core task and the two extension tasks.

This paper presents the design of Glow, a machine learning compiler for heterogeneous hardware. It is a pragmatic approach to compilation that enables the generation of highly optimized code for multiple targets. Glow lowers the traditional…

Tensor train (TT) representation has achieved tremendous success in visual data completion tasks, especially when it is combined with tensor folding. However, folding an image or video tensor breaks the original data structure, leading to…

Graph clustering, aiming to partition nodes of a graph into various groups via an unsupervised approach, is an attractive topic in recent years. To improve the representative ability, several graph auto-encoder (GAE) models, which are based…

Network alignment is useful for multiple applications that require increasingly large graphs to be processed. Existing research approaches this as an optimization problem or computes the similarity based on node representations. However,…

Answering complex logical queries on incomplete knowledge graphs is a challenging task, and has been widely studied. Embedding-based methods require training on complex queries, and cannot generalize well to out-of-distribution query…