C의 저수준 기능과 컴파일러 최적화 조화시키기

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2019. 2. 허충길.주류 C 컴파일러들은 프로그램의 성능을 높이기 위해 공격적인 최적화를 수행하는데, 그런 최적화는 저수준 기능을 사용하는 프로그램의 행동을 바꾸기도 한다. 불행히도 C 언어를 디자인할 때 저수준 기능과 컴파일러 최적화를 적절하게 조화시키가 굉장히 어렵다는 것이 학계와 업계의 중론이다. 저수준 기능을 위해서는, 그러한 기능이 시스템 프로그래밍에 사용되는 패턴을 잘 지원해야 한다. 컴파일러 최적화를 위해서는, 주류 컴파일러가 수행하는 복잡하고도 효과적인 최적화를 잘 지원해야 한다. 그러나 저수준 기능과 컴파일러 최적화를 동시에 잘 지원하는 실행의미는 오늘날까지 제안된 바가 없다. 본 박사학위 논문은 시스템 프로그래밍에서 요긴하게 사용되는 저수준 기능과 주요한 컴파일러 최적화를 조화시킨다. 구체적으로, 우린 다음 성질을 만족하는 느슨한 동시성, 분할 컴파일, 정수-포인터 변환의 실행의미를 처음으로 제안한다. 첫째, 기능이 시스템 프로그래밍에서 사용되는 패턴과, 그러한 패턴을 논증할 수 있는 기법을 지원한다. 둘째, 주요한 컴파일러 최적화들을 지원한다. 우리가 제안한 실행의미에 자신감을 얻기 위해 우리는 논문의 주요 결과를 대부분 Coq 증명기 위에서 증명하고, 그 증명을 기계적이고 엄밀하게 확인했다.To improve the performance of C programs, mainstream compilers perform aggressive optimizations that may change the behaviors of programs that use low-level features in unidiomatic ways. Unfortunately, despite many years of research and industrial efforts, it has proven very difficult to adequately balance the conflicting criteria for low-level features and compiler optimizations in the design of the C programming language. On the one hand, C should support the common usage patterns of the low-level features in systems programming. On the other hand, C should also support the sophisticated and yet effective optimizations performed by mainstream compilers. None of the existing proposals for C semantics, however, sufficiently support low-level features and compiler optimizations at the same time. In this dissertation, we resolve the conflict between some of the low-level features crucially used in systems programming and major compiler optimizations. Specifically, we develop the first formal semantics of relaxed-memory concurrency, separate compilation, and cast between integers and pointers that (1) supports their common usage patterns and reasoning principles for programmers, and (2) provably validates major compiler optimizations at the same time. To establish confidence in our formal semantics, we have formalized most of our key results in the Coq theorem prover, which automatically and rigorously checks the validity of the results.Abstract Acknowledgements Chapter I Prologue Chapter II Relaxed-Memory Concurrency Chapter III Separate Compilation and Linking Chapter IV Cast between Integers and Pointers Chapter V Epilogue 초록Docto

MetaFork: A Compilation Framework for Concurrency Models Targeting Hardware Accelerators

Parallel programming is gaining ground in various domains due to the tremendous computational power that it brings; however, it also requires a substantial code crafting effort to achieve performance improvement. Unfortunately, in most cases, performance tuning has to be accomplished manually by programmers. We argue that automated tuning is necessary due to the combination of the following factors. First, code optimization is machine-dependent. That is, optimization preferred on one machine may be not suitable for another machine. Second, as the possible optimization search space increases, manually finding an optimized configuration is hard. Therefore, developing new compiler techniques for optimizing applications is of considerable interest. This thesis aims at generating new techniques that will help programmers develop efficient algorithms and code targeting hardware acceleration technologies, in a more effective manner. Our work is organized around a compilation framework, called MetaFork, for concurrency platforms and its application to automatic parallelization. MetaFork is a high-level programming language extending C/C++, which combines several models of concurrency including fork-join, SIMD and pipelining parallelism. MetaFork is also a compilation framework which aims at facilitating the design and implementation of concurrent programs through four key features which make MetaFork unique and novel: (1) Perform automatic code translation between concurrency platforms targeting multi-core architectures. (2) Provide a high-level language for expressing concurrency as in the fork-join model, the SIMD paradigm and the pipelining parallelism. (3) Generate parallel code from serial code with an emphasis on code depending on machine or program parameters (e.g. cache size, number of processors, number of threads per thread block). (4) Optimize code depending on parameters that are unknown at compile-time