PDF-Scalable Autotuning ramew ork for Compiler Optimization Anan ta Tiw ari Ch un Chen Jacqueline

Hollingsw orth Univ ersit of Maryland Univ ersit of Utah Departmen of Computer Science Sc ho ol of Computing College ark MD 20740 Salt Lak Cit UT 84112 tiw ari hollings csumdedu unc hen mhall csutahedu Univ ersit of Southern California Information S. NYCLU NYCLU EW ORK IVI IBERTIES UN IO EW ORK IVI IBERTIES UN IO NYCLU NYCLU EW ORK IVI IBERTIES UN IO EW ORK IVI IBERTIES UN IO 1 Introduction Any autotuning procedure starts by taking inputoutput measurements from the pro cess This can be done in open or closedloop by deliberately injecting some stim ulus or simply relying on the excitation provided by standard manoeuvres li In the presen ork the detailed statistical analysis of the strength data is carried out using larger class of probabilit mo dels including eibull normal lognormal gamma and generalized exp onen tial distributions Our analysis is alidated using the s Fred D. Lublin, MD . Saunders Family Professor . of Neurology. Director, The Corinne Goldsmith Dickinson Center . for Multiple Sclerosis. The Icahn School of Medicine. New York, NY. Krzysztof Selmaj, . Association des groupes amiti Nuno Lopes . and. José Monteiro. Deriving preconditions by hand is hard; WPs are often non-trivial. WPs derived by hand are often wrong!. Weaker preconditions expose more optimization opportunities. Computational . Exascale. Workshop. December 2010. Dan Quinlan. Chunhua. Liao, Justin Too, Robb . Matzke. , Peter . Pirkelbauer. Center for Applied Scientific Computing. Lawrence Livermore National Laboratory. Luis . Herranz. Arribas. Supervisor: Dr. José M. Martínez Sánchez. Video Processing and Understanding Lab. Universidad . Aut. ónoma. de Madrid. Outline. Introduction. Integrated. . summarization. Prof. O. . Nierstrasz. Lecture notes by Marcus . Denker. © Marcus . Denker. Optimization. Roadmap. Introduction. Optimizations in the Back-end. The Optimizer. SSA Optimizations. Advanced Optimizations. Larry Peterson. In collaboration with . Arizona. , Akamai. ,. . Internet2. , NSF. , North Carolina, . Open Networking Lab, Princeton. (and several pilot sites). S3. DropBox. GenBank. iPlant. Data Management Challenge. usually machine language, sometimes assembly language as an intermediate. for Java and . .net. , compilers convert code into an intermediate format. a just-in-time (JIT) compiler or interpreter then converts it into machine code. Prof. Gennady . Pekhimenko. University of Toronto. Winter 2018. The content of this lecture is adapted from the lectures of . Todd Mowry and Phillip Gibbons. CSC D70: . Compiler Optimization. Introduction, Logistics. Classification of algorithms. The DIRECT algorithm. Divided rectangles. Exploration and Exploitation as bi-objective optimization. Application to High Speed Civil Transport. Global optimization issues. Large scale computing systems. Scalability . issues. Low level and high level communication abstractions in scalable systems. Network interface . Common techniques for high performance communication.