Tiark Rompf
ABSTRACT
Recent years have seen a surge of interest in staging and generative programming, driven by the increasing difficulty of making high-level code run fast on modern hardware. While the mechanics of program generation are relatively well understood, we have only begun to understand how to develop systems in a generative way. The Lightweight Modular Staging (LMS) platform forms the core of a research agenda to make generative programming more widely accessible, through powerful libraries and a growing selection of case studies that illuminate design patterns and crystallize best practices for high-level and effective generative programming. This talk will reflect on the foundations of LMS, on applications, achievements, challenges, as well as ongoing and future work.
Index Terms
- Lightweight modular staging (LMS): generate all the things! (keynote)
Recommendations
Lightweight modular staging: a pragmatic approach to runtime code generation and compiled DSLs
GPCE '10: Proceedings of the ninth international conference on Generative programming and component engineeringSoftware engineering demands generality and abstraction, performance demands specialization and concretization. Generative programming can provide both, but the effort required to develop high-quality program generators likely offsets their benefits, ...
Lightweight modular staging (LMS): generate all the things! (keynote)
GPCE '16Recent years have seen a surge of interest in staging and generative programming, driven by the increasing difficulty of making high-level code run fast on modern hardware. While the mechanics of program generation are relatively well understood, we ...
Colon cancer diagnosis and staging classification based on machine learning and bioinformatics analysis
AbstractAdvanced metastasis of colon cancer makes it more difficult to treat colon cancer. Finding the markers of colon cancer (Colon Cancer) can diagnose the stage of cancer in time and improve the prognosis with timely treatment. This paper uses gene ...
Highlights- Machine learning analysis combined with bioinformatics analysis to screen for markers.
- Random forests perform best in both colon cancer and colon cancer staging diagnosis.
- The accuracy of colon cancer diagnosis is greater than 98%.
Comments