Phoenix compiler research papers
The Phoenix Compiler and Tools Framework - ppt download
Using state-of-the art equipment and techniques, we are able to provide clear, accurate, and objective data that goes beyond personal opinions and assumptions. Our consultants can assist you with research design, data analysis and visualization, and PhD-level advice by qualified, publishing researchers. Intelligent Interactive Instructional Systems Kurt VanLehn Finding methods for combing human and machine intelligence to increase student learning.
Knowledge Representation and BioAI lab Chitta Baral Representation and reasoning with knowledge, natural language understanding and applications to molecular biology. Interests in medium access control protocols and higher layer protocols, cross-layer design and optimization, applying techniques from combinatorics, and designed experiments. ONRL Optimization and Networking Research Laboratory Guoliang Xue ONRL is home to many dedicated graduate students and undergraduate students, conducting high quality research in many areas, including human-centric computing, crowdsourcing to smart phones, privacy and security, robustness and survivability; and optimal resource allocation.
We aspire to provide real-time and energy-efficient computing solutions for data analytics and information processing. Current research interests include identity management and access control, formal models for computer security, network and distributed systems security including mobile and cloud computing, vulnerability and risk assessment and cybercrime analysis among others. Richa Investigating the theoretical and algorithmic underpinnings of programmable matter, largely through the lens of distributed computing and randomized algorithms.
Motivated by the need to perform neural network inference on encrypted medical and financial data, CHET supports a domain-specific language for specifying tensor circuits. It automates many of the laborious and error prone tasks of encoding such circuits homomorphically, including encryption parameter selection to guarantee security and accuracy of the computation, determining efficient tensor layouts, and performing scheme-specific optimizations.
Our evaluation on a collection of popular neural networks shows that CHET generates homomorphic circuits that outperform expert-tuned circuits and makes it easy to switch across different encryption schemes. We demonstrate its scalability by evaluating it on a version of SqueezeNet, which to the best of our knowledge, is the deepest neural network to be evaluated homomorphically.
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For example:. I am continuing my work improving the Apple developer tools, and continue to contribute daily to the open source LLVM technologies. Xcode 4.
ARC has revolutionized Objective-C programming by automating memory management without the runtime overhead of a garbage collector. I personally defined and drove this feature late in the schedule of iOS5 and Lion. This is notable for the short schedule for the project, the extensive cross-functional work required, and the extensive backwards compatibility issues that had to be addressed making it a very technically complex problem.
Xcode 4 itself now features deep integration of the Clang parser for code completion, syntax highlighting, indexing, live warning and error messages, and the new 'Fix-It' feature in which the compiler informs the UI how to automatically corrects small errors. The Xcode 4 preview also includes the first public release of LLDB to which I served as a consultant and contributed directly to turning it into an open source project. During this period my team brought Clang 1.
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- Phoenix compiler research papers!
We also productized and shipped the Xcode static analyzer, a new compiler-rt library which replaced libgcc in Snow Leopard and many enhancements to existing components in the operating system. We shipped llvm-gcc 4. In addition to llvm-gcc, much of the work during this time was focused on Mac OS I made major contributions to design and implementation of the "Blocks" language feature as well as to the architecture and design of the language and compiler aspects of the OpenCL GPGPU technology.
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Finally, during this period I architected and started implementation of a suite of front-end technologies based on LLVM, named " Clang ". My main contributions during this time was a new llvm-gcc4 front-end, significant improvements to the X86 and PowerPC backends, a wide range of optimization improvements and new optimizers, significant improvement to the target-independent code generator, and leadership for the rest of the team.
At Illinois, I designed and built most of the fundamental aspects of LLVM, establishing the architecture for things to come and building most of the scalar, loop and interprocedural optimizers. I also built most of the target-independent code generator, X86 backend, JIT, llvm-gcc3 front-end, and much more.
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Finally, I wrote many papers.