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Last time, we saw how deleting stuff from a test case can be an easy and fun route to the root cause of a bug. It’s less easy and less fun when the test cases get big. The inner loop of test-case reduction can get old quickly: delete stuff, run the special command, check the output to decide whether to backtrack or proceed. It’s rote, mechanical, and annoyingly error prone. Let’s make the computer
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Schedule Syllabus Lessons Zulip Discussions Blog CS 6120: Advanced Compilers: The Self-Guided Online Course CS 6120 is a PhD-level Cornell CS course by Adrian Sampson on programming language implementation. It covers universal compilers topics like intermediate representations, data flow, and “classic” optimizations as well as more research-flavored topics such as parallelization, just-in-time com
Flattening ASTs (and Other Compiler Data Structures) May 1, 2023 Normal and flattened ASTs for the expression a * b + c. Arenas, a.k.a. regions, are everywhere in modern language implementations. One form of arenas is both super simple and surprisingly effective for compilers and compiler-like things. Maybe because of its simplicity, I haven’t seen the basic technique in many compiler courses—or a
Syllabus Schedule Lessons Zulip Blog CS 6120: Advanced Compilers: The Self-Guided Online Course CS 6120 is a PhD-level Cornell CS course by Adrian Sampson on programming language implementation. It covers universal compilers topics like intermediate representations, data flow, and “classic” optimizations as well as more research-flavored topics such as parallelization, just-in-time compilation, an
Data Structures and Functional Programming Spring 2020 Why Learn About Functional Programming? Assignments Programming assignments Reflections Project Getting Help Installing OCaml for 3110 Help from the course staff Help on OCaml Textbook The 3110 textbook Resources CMS: assignments Campuswire: announcements You can add yourself to Campuswire. You will automatically be added to CMS within a day o
MegaDepth: Learning Single-View Depth Prediction from Internet Photos We use large Internet image collections, combined with 3D reconstruction and semantic labeling methods, to generate large amounts of training data for single-view depth prediction. (a), (b), (e): Example input RGB images. (c), (d), (f): Depth maps predicted by our MegaDepth-trained CNN (blue=near, red=far). For these results, th
LLVM for Grad Students August 3, 2015 This is an introduction to doing research with the LLVM compiler infrastructure. It should be enough for a grad student to go from mostly uninterested in compilers to excited to use LLVM to do great work. What is LLVM? LLVM is a compiler. It’s a really nice, hackable, ahead-of-time compiler for “native” languages like C and C++. Of course, since LLVM is so awe
Designs, Lessons and Advice from Building Large Distributed Systems Jeff Dean Google Fellow jeff@google.com Computing shifting to really small and really big devices UI-centric devices Large consolidated computing farms Google’s data center at The Dalles, OR The Machinery Servers • CPUs • DRAM • Disks Racks • 40-80 servers • Ethernet switch Clusters Architectural view of the storage hierarchy … P
The programming languages and machine learning communities have, over the last few years, developed a shared set of research interests under the umbrella of probabilistic programming. The idea is that we might be able to “export” powerful PL concepts like abstraction and reuse to statistical modeling, which is currently an arcane and arduous task. (You may want to read the most recent version of t
Statistical Mistakes and How to Avoid Them November 23, 2016 Computer scientists in systemsy fields, myself included, aren’t great at using statistics. Maybe it’s because there are so many other potential problems with empirical evaluations that solid statistical reasoning doesn’t seem that important. Other subfields, like HCI and machine learning, have much higher standards for data analysis. Let
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SWIM: Scalable Weakly-consistent Infection-style Process Group Membership Protocol Abhinandan Das, Indranil Gupta, Ashish Motivala Dept. of Computer Science, Cornell University Ithaca NY 14853 USA¡ asdas,gupta,ashish¢ @cs.cornell.edu Abstract Several distributed peer-to-peer applications require weakly-consistent knowledge of process group membership information at all participating processes.
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An Analysis of Facebook Photo Caching Qi Huang∗, Ken Birman∗, Robbert van Renesse∗, Wyatt Lloyd†‡, Sanjeev Kumar‡, Harry C. Li‡ ∗Cornell University, †Princeton University, ‡Facebook Inc. Abstract This paper examines the workload of Facebook’s photo- serving stack and the effectiveness of the many layers of caching it employs. Facebook’s image-management infrastructure is complex and geographically
Stitch Meshes for Modeling Knitted Clothing with Yarn-level Detail Stages of our knitted garment modeling system: (a) We begin our interactive modeling process with a polygonal mesh that specifies the global shape of the cloth model; (b) using this polygonal mesh we produce a high-resolution stitch mesh that serves as a canvas-like abstraction of the yarn model; (c) then, we specify the desired kni
About Nori is a simple ray tracer written in C++. It runs on Windows, Linux, and Mac OS and provides basic functionality that is required to complete the projects in CS6630. While Nori provides much support code to simplify your development work as much as possible, the code that you will initially receive from us in fact does very little: it renders a simple scene with ambient occlusion and write
Contact sound models based on linear modal analysis are commonly used with rigid body dynamics. Unfortunately, treating vibrating objects as “rigid” during collision and contact processing fundamentally limits the range of sounds that can be computed, and contact solvers for rigid body animation can be ill-suited for modal contact sound synthesis, producing various sound artifacts. In this paper,
Systems for Large Data CS 6322 Department of Computer Science Cornell University Fall 2008 Instructor: Johannes Gehrke Time: Tuesdays and Thursdays, 2:45-4:00pm. Place: Thurston 202 Course Management System News Course Overview The last decade has been a turning point in database research. The number of research communities working on BIG data has grown significantly, and it now not only includes
Software for Dynamic Ranking Christina Brandt & Jacob Bank ccb35@cornell.edu, jeb369@cornell.edu 1 Introduction Most queries issued to a search engine are ambiguous at some level. This presents the search engine with a dilemma. On the one hand, if it focuses on the most likely interpretation of the query, it does not provide any utility to users that had a different intent. On the other hand, if
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Cornell Tech and Google DeepMind Cornell University, Department of Computer Science Bloomberg Room 365 2 West Loop Road New York, NY 10044 e: snavely@cs.cornell.edu @GitHub Useful Links: Crowdsampling Datasets Latex Style Guide Fmatrix Demo MegaDepth Landmarks10k & San Francisco Datasets Scene Chronology BigSFM 3D Reconstruction Videos: Uris Hall, Cornell Dubrovnik Dubrovnik Time-Lapse Roman
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Stephen R. Marschner, Henrik Wann Jensen, Mike Cammarano, Steve Worley, and Pat Hanrahan. In Proceedings of SIGGRAPH 2003. Held in San Diego, California, August 2003. (the canonical page for this paper) Abstract Light scattering from hair is normally simulated in computer graphics using Kajiya and Kay's classic phenomenological model. We have made new measurements of scattering from individual hai
High School Dating (Bearman, Moody, and Stovel, 2004) (Image by Mark Newman) In recent years there has been a growing public fascination with the complex "connectedness" of modern society. This connectedness is found in many incarnations: in the rapid growth of the Internet and the Web, in the ease with which global communication now takes place, and in the ability of news and information as well
Typed Assembly Language (TAL) extends traditional untyped assembly languages with typing annotations, memory management primitives, and a sound set of typing rules. These typing rules guarantee the memory safety, control flow safety, and type safety of TAL programs. Moreover, the typing constructs are expressive enough to encode most source language programming features including records and str
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