An llVM backend for GHC

Terei, David; Chakravarty, Manuel M. T.

doi:10.1145/1863523.1863538

Cited by 23 publications

(15 citation statements)

References 9 publications

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“…LLVM now also supports GC implementation through a compiler plug-in. Terei and Chakravarty give a more detailed comparison between Cmm and LLVM [28].…”

Section: Related and Future Workmentioning

confidence: 99%

The Intel labs Haskell research compiler

Liu

Glew

Petersen

et al. 2013

Proceedings of the 2013 ACM SIGPLAN Symposium on Haskell

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The Glasgow Haskell Compiler (GHC) is a well supported optimizing compiler for the Haskell programming language, along with its own extensions to the language and libraries. Haskell's lazy semantics imposes a runtime model which is in general difficult to implement efficiently. GHC achieves good performance across a wide variety of programs via aggressive optimization taking advantage of the lack of side effects, and by targeting a carefully tuned virtual machine. The Intel Labs Haskell Research Compiler uses GHC as a frontend, but provides a new whole-program optimizing backend by compiling the GHC intermediate representation to a relatively generic functional language compilation platform. We found that GHC's external Core language was relatively easy to use, but reusing GHC's libraries and achieving full compatibility were harder. For certain classes of programs, our platform provides substantial performance benefits over GHC alone, performing 2× faster than GHC with the LLVM backend on selected modern performance-oriented benchmarks; for other classes of programs, the benefits of GHC's tuned virtual machine continue to outweigh the benefits of more aggressive whole program optimization. Overall we achieve parity with GHC with the LLVM backend. In this paper, we describe our Haskell compiler stack, its implementation and optimization approach, and present benchmark results comparing it to GHC.

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“…LLVM now also supports GC implementation through a compiler plug-in. Terei and Chakravarty give a more detailed comparison between Cmm and LLVM [28].…”

Section: Related and Future Workmentioning

confidence: 99%

The Intel labs Haskell research compiler

Liu

Glew

Petersen

et al. 2013

Proceedings of the 2013 ACM SIGPLAN Symposium on Haskell

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“…The LLVM system [12] is a runtime used in both research and real-world applications, with compiler support for numerous languages. These include languages that have been the focus of the work on type systems mentioned above [19,20]. We believe that this will lead to interesting applications using the combination of secure computation and static analysis techniques.…”

Section: Llvmmentioning

confidence: 99%

Lessons learned with PCF

Kreuter

Shelat

2013

Proceedings of the First ACM Workshop on Language Support for Privacy-Enhancing Technologies

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The Portable Circuit Format (PCF) system presented by Kreuter et al. [10] addressed an important bottleneck in the tools developed by the research community for secure computation. The PCF system is based on the idea that circuits can be represented as programs that emit gates on certain state transitions. By dealing with programs rather than circuits, functions have much smaller representations, the PCF compiler is able to apply automatic optimizations to much larger functions, and the runtime system requires far less memory. We present our future plans for the PCF system and our current work-in-progress.

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“…If there is any doubt that this results in efficient machine code, we give the actual assembly language inner loop output by GHC using the LLVM back end [31]. Stream fusion preserves the ability to write compositionally without sacrificing performance.…”

Section: Fusing the Vector Dot Productmentioning

confidence: 99%

Exploiting vector instructions with generalized stream fusion

Mainland

Leshchinskiy²,

Jones

2013

Proceedings of the 18th ACM SIGPLAN International Conference on Functional Programming

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Stream fusion [6] is a powerful technique for automatically transforming high-level sequence-processing functions into efficient implementations. It has been used to great effect in Haskell libraries for manipulating byte arrays, Unicode text, and unboxed vectors. However, some operations, like vector append, still do not perform well within the standard stream fusion framework. Others, like SIMD computation using the SSE and AVX instructions available on modern x86 chips, do not seem to fit in the framework at all.In this paper we introduce generalized stream fusion, which solves these issues. The key insight is to bundle together multiple stream representations, each tuned for a particular class of stream consumer. We also describe a stream representation suited for efficient computation with SSE instructions. Our ideas are implemented in modified versions of the GHC compiler and vector library. Benchmarks show that high-level Haskell code written using our compiler and libraries can produce code that is faster than both compiler-and hand-vectorized C.

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An llVM backend for GHC

Cited by 23 publications

References 9 publications

The Intel labs Haskell research compiler

The Intel labs Haskell research compiler

Lessons learned with PCF

Exploiting vector instructions with generalized stream fusion

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