Compiler

Summary

In computing, a compiler is a computer program that translates computer code written in one programming language (the source language) into another language (the target language). The name "compiler" is primarily used for programs that translate source code from a high-level programming language to a low-level programming language (e.g. assembly language, object code, or machine code) to create an executable program. There are many different types of compilers which produce output in different useful forms. A cross-compiler produces code for a different CPU or operating system than the one on which the cross-compiler itself runs. A bootstrap compiler is often a temporary compiler, used for compiling a more permanent or better optimised compiler for a language. Related software include, a program that translates from a low-level language to a higher level one is a decompiler ; a program that translates between high-level languages, usually called a source-to-source compiler or tran

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https://en.wikipedia.org/wiki/Compiler

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Fully-Homomorphic Encryption (FHE) offers powerful capabilities by enabling secure offloading of both storage and computation, and recent innovations in schemes and implementations have made it all the more attractive. At the same time, FHE is notoriously hard to use with a very constrained programming model, a very unusual performance profile, and many cryptographic constraints. Existing compilers for FHE either target simpler but less efficient FHE schemes or only support specific domains where they can rely on expert-provided high-level runtimes to hide complications. This paper presents a new FHE language called Encrypted Vector Arithmetic (EVA), which includes an optimizing compiler that generates correct and secure FHE programs, while hiding all the complexities of the target FHE scheme. Bolstered by our optimizing compiler, programmers can develop efficient general-purpose FHE applications directly in EVA. For example, we have developed image processing applications using EVA, with a very few lines of code. EVA is designed to also work as an intermediate representation that can be a target for compiling higher-level domain-specific languages. To demonstrate this, we have re-targeted CHET, an existing domain-specific compiler for neural network inference, onto EVA. Due to the novel optimizations in EVA, its programs are on average 5.3x faster than those generated by CHET. We believe that EVA would enable a wider adoption of FHE by making it easier to develop FHE applications and domain-specific FHE compilers.

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Better Loop Fusion for LMS

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This is my master thesis done at PPL in Stanford under the supervision of Prof. Kunle Olukotun. It improved LMS, a framework for embedding DSLs (domain-specific languages) into Scala which features many general optimizations that can be used by any DSLs for free. I implemented a more powerful and cleaner version of the loop fusion optimization from the compiler world. Loop fusion is an important performance optimization for all languages that feature list comprehensions and translate their high-level operations into loop-based representations. It can decrease runtime, memory footprint and code size through two different fusion cases: The simpler one is called horizontal or side-by-side fusion and fuses adjacent loops iterating over the same range, enabling further optimizations. The second one is vertical or pipeline fusion, where a producer and a consumer of data are fused, removing the need for the intermediate data structure.

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Auto-Collections for Scala

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Modern programming languages such as Scala, Java and C# make extensive use of collections. A collection implementation represents a fixed choice in the dimensions of operation time and space utilization. Using the collection in a manner not consistent with this fixed choice can cause significant performance degradation. Often programmers have to choose between under-performing generic collection and specific data-structures at the cost of modularity and ex- tensibility. In his paper, we present and evaluate a generic collection library that automatically choose the appropriate collection implementations for an application. We implemented our solution in the Dotty compiler for Scala and showed that our implementation can choose optimal collections for com- mon basic algorithms.

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