Tapir
| Autor: | William S. Moses, Tao B. Schardl, Charles E. Leiserson |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Optimizing compiler 02 engineering and technology Cilk Fork–join queue computer.software_genre 01 natural sciences Control flow biology.animal 0103 physical sciences 0202 electrical engineering electronic engineering information engineering computer.programming_language 010302 applied physics 020203 distributed computing Intermediate language Multi-core processor Recursion biology Programming language computer.file_format Computer Science Applications Loop scheduling Computational Theory and Mathematics Hardware and Architecture Modeling and Simulation Control flow graph Executable Compiler Software_PROGRAMMINGLANGUAGES Tapir computer Software |
| Zdroj: | ACM Transactions on Parallel Computing. 6:1-33 |
| ISSN: | 2329-4957 2329-4949 |
| Popis: | Tapir (pronounced TAY-per) is a compiler intermediate representation (IR) that embeds recursive fork-join parallelism, as supported by task-parallel programming platforms such as Cilk and OpenMP, into a mainstream compiler’s IR. Mainstream compilers typically treat parallel linguistic constructs as syntactic sugar for function calls into a parallel runtime. These calls prevent the compiler from performing optimizations on and across parallel control constructs. Remedying this situation has generally been thought to require an extensive reworking of compiler analyses and code transformations to handle parallel semantics. Tapir leverages the “serial-projection property,” which is commonly satisfied by task-parallel programs, to handle the semantics of these programs without an extensive rework of the compiler. For recursive fork-join programs that satisfy the serial-projection property, Tapir enables effective compiler optimization of parallel programs with only minor changes to existing compiler analyses and code transformations. Tapir uses the serial-projection property to order logically parallel fine-grained tasks in the program’s control-flow graph. This ordered representation of parallel tasks allows the compiler to optimize parallel codes effectively with only minor modifications. For example, to implement Tapir/LLVM, a prototype of Tapir in the LLVM compiler, we added or modified less than 3,000 lines of LLVM’s half-million-line core middle-end functionality. These changes sufficed to enable LLVM’s existing compiler optimizations for serial code—including loop-invariant-code motion, common-subexpression elimination, and tail-recursion elimination—to work with parallel control constructs such as parallel loops and Cilk’s Cilk_Spawn keyword. Tapir also supports parallel optimizations, such as loop scheduling, which restructure the parallel control flow of the program. By making use of existing LLVM optimizations and new parallel optimizations, Tapir/LLVM can optimize recursive fork-join programs more effectively than traditional compilation methods. On a suite of 35 Cilk application benchmarks, Tapir/LLVM produces more efficient executables for 30 benchmarks, with faster 18-core running times for 26 of them, compared to a nearly identical compiler that compiles parallel linguistic constructs the traditional way. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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