Showing papers on "Source transformation published in 2019"

Target-Tailored Source-Transformation for Scene Graph Generation

Abstract: Scene graph generation aims to provide a semantic and structural description of an image, denoting the objects (with nodes) and their relationships (with edges). The best performing works to date are based on exploiting the context surrounding objects or relations,e.g., by passing information among objects. In these approaches, to transform the representation of source objects is a critical process for extracting information for the use by target objects. In this work, we argue that a source object should give what tar-get object needs and give different objects different information rather than contributing common information to all targets. To achieve this goal, we propose a Target-TailoredSource-Transformation (TTST) method to efficiently propagate information among object proposals and relations. Particularly, for a source object proposal which will contribute information to other target objects, we transform the source object feature to the target object feature domain by simultaneously taking both the source and target into account. We further explore more powerful representations by integrating language prior with the visual context in the transformation for the scene graph generation. By doing so the target object is able to extract target-specific information from the source object and source relation accordingly to refine its representation. Our framework is validated on the Visual Genome bench-mark and demonstrated its state-of-the-art performance for the scene graph generation. The experimental results show that the performance of object detection and visual relation-ship detection are promoted mutually by our method.

Software Engineering by Source Transformation – Experience with TXL (Most Influential Paper, SCAM 2001)

Abstract: Most influential paper of SCAM 2001.

Efficiency and expressiveness in UW-OpenMP

Abstract: OpenMP uses the efficient ‘team of workers’ model, where workers are given chunks of tasks (iterations of a parallel-for-loop, or sections in a parallel-sections block) to execute, and worker (not tasks) can be synchronized using barriers. Thus, OpenMP restricts the invocation of barriers in these tasks; as otherwise, the behavior of the program would be dependent on the number of runtime workers. To address such a restriction which can adversely impact programmability and readability, Aloor and Nandivada proposed UW-OpenMP by taking inspiration from the more intuitive interaction of tasks and barriers in newer task parallel languages like X10, HJ, Chapel and so on. UW-OpenMP gives the programmer an impression that each parallel task is executed by a unique worker, and importantly these parallel tasks can be synchronized using a barrier construct. Though UW-OpenMP is a useful extension of OpenMP (more expressive and efficient), it does not admit barriers within recursive functions invoked from parallel-for-loops, because of the inherent challenges in handing them. In this paper, we extend UW-OpenMP (we call it UWOmp++) to address this challenging limitation and in the process also realize more efficient programs. We propose a source to source transformation scheme to translate UWOmp++ C programs to equivalent OpenMP C programs that are guaranteed not to invoke barriers in any task. Our translation uses a novel intermediate representation called UWOmpCPS, which represents a parallel program written in OpenMP in an extended CPS format (admits parallel-for-loops and barriers). The use of this intermediate representation leads us to handle recursive functions within parallel-for-loops efficiently. We have implemented our proposed translation scheme in the ROSE compiler framework. Our preliminary evaluation shows that the proposed language extension to allow recursion helps plug an important gap in expressiveness, without compromising on the efficiency resulting from the ‘team of workers’ model.

Source-to-source transformation based methodology for graph-parallel FPGA accelerators

Abstract: SOURCE-TO-SOURCE TRANSFORMATION BASED METHODOLOGY FOR GRAPH-PARALLEL FPGA ACCELERATORS Cemil Kaan Akyol M.S. in Computer Engineering Advisor: Özcan Öztürk August 2019 Graph applications are becoming more and more important with their widespread usage and the amounts of data they deal with. Biological and social web graphs are well-known examples which show the importance of efficient processing of the graph analytic applications and problems. Addressing those problems in an efficient manner is not a straightforward task. Distributing and parallelizing the computation, and integrating hardware accelerators are the main approaches that were tried during the last decade. However, these approaches mainly focus on specific legacy algorithms and may not completely solve the problems. Therefore, when there is an emerging need for a non-legacy algorithm targeting a specific problem, the developer has to cope with the adversaries of the distribution, parallelization techniques, and hardware specifications to parallelize and accelerate the application. Our proposed source-to-source based methodology gives the freedom of not knowing the low-level details of parallelization and distribution by translating any vertex-centric C++ graph application into pipelined SystemC model. In order to support different types of graph applications, we have implemented several features like non-standard application support, active set functionality, multi-pipeline support, etc. The generated SystemC model can be synthesized by High-Level Synthesis (HLS) tools to obtain the FPGA programming image, i.e., the bitstream. Our accelerator development flow can generate two different execution models, high-throughput (HT) and work-efficient (WE). Compared to OpenCL counterparts of the algorithms, HT and WE models perform slightly better in terms of execution time and throughput. WE model performed approximately 40% better than OpenCL in terms of work done and execution time. Therefore, the proposed source-to-source based methodology is able to provide more efficient hardware designs by only requiring a simple high-level language description from the user. iii