An LLVM Backend for GHC
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
The LLVM Instruction Set and Compilation Strategy
The LLVM Instruction Set and Compilation Strategy Chris Lattner Vikram Adve University of Illinois at Urbana-Champaign lattner,vadve ¡ @cs.uiuc.edu Abstract This document introduces the LLVM compiler infrastructure and instruction set, a simple approach that enables sophisticated code transformations at link time, runtime, and in the field. It is a pragmatic approach to compilation, interfering with programmers and tools as little as possible, while still retaining extensive high-level information from source-level compilers for later stages of an application’s lifetime. We describe the LLVM instruction set, the design of the LLVM system, and some of its key components. 1 Introduction Modern programming languages and software practices aim to support more reliable, flexible, and powerful software applications, increase programmer productivity, and provide higher level semantic information to the compiler. Un- fortunately, traditional approaches to compilation either fail to extract sufficient performance from the program (by not using interprocedural analysis or profile information) or interfere with the build process substantially (by requiring build scripts to be modified for either profiling or interprocedural optimization). Furthermore, they do not support optimization either at runtime or after an application has been installed at an end-user’s site, when the most relevant information about actual usage patterns would be available. The LLVM Compilation Strategy is designed to enable effective multi-stage optimization (at compile-time, link-time, runtime, and offline) and more effective profile-driven optimization, and to do so without changes to the traditional build process or programmer intervention. LLVM (Low Level Virtual Machine) is a compilation strategy that uses a low-level virtual instruction set with rich type information as a common code representation for all phases of compilation. -
GHC Reading Guide
GHC Reading Guide - Exploring entrances and mental models to the source code - Takenobu T. Rev. 0.01.1 WIP NOTE: - This is not an official document by the ghc development team. - Please refer to the official documents in detail. - Don't forget “semantics”. It's very important. - This is written for ghc 9.0. Contents Introduction 1. Compiler - Compilation pipeline - Each pipeline stages - Intermediate language syntax - Call graph 2. Runtime system 3. Core libraries Appendix References Introduction Introduction Official resources are here GHC source repository : The GHC Commentary (for developers) : https://gitlab.haskell.org/ghc/ghc https://gitlab.haskell.org/ghc/ghc/-/wikis/commentary GHC Documentation (for users) : * master HEAD https://ghc.gitlab.haskell.org/ghc/doc/ * latest major release https://downloads.haskell.org/~ghc/latest/docs/html/ * version specified https://downloads.haskell.org/~ghc/9.0.1/docs/html/ The User's Guide Core Libraries GHC API Introduction The GHC = Compiler + Runtime System (RTS) + Core Libraries Haskell source (.hs) GHC compiler RuntimeSystem Core Libraries object (.o) (libHsRts.o) (GHC.Base, ...) Linker Executable binary including the RTS (* static link case) Introduction Each division is located in the GHC source tree GHC source repository : https://gitlab.haskell.org/ghc/ghc compiler/ ... compiler sources rts/ ... runtime system sources libraries/ ... core library sources ghc/ ... compiler main includes/ ... include files testsuite/ ... test suites nofib/ ... performance tests mk/ ... build system hadrian/ ... hadrian build system docs/ ... documents : : 1. Compiler 1. Compiler Compilation pipeline 1. compiler The GHC compiler Haskell language GHC compiler Assembly language (native or llvm) 1. compiler GHC compiler comprises pipeline stages GHC compiler Haskell language Parser Renamer Type checker Desugarer Core to Core Core to STG STG to Cmm Assembly language Cmm to Asm (native or llvm) 1. -
What Is LLVM? and a Status Update
What is LLVM? And a Status Update. Approved for public release Hal Finkel Leadership Computing Facility Argonne National Laboratory Clang, LLVM, etc. ✔ LLVM is a liberally-licensed(*) infrastructure for creating compilers, other toolchain components, and JIT compilation engines. ✔ Clang is a modern C++ frontend for LLVM ✔ LLVM and Clang will play significant roles in exascale computing systems! (*) Now under the Apache 2 license with the LLVM Exception LLVM/Clang is both a research platform and a production-quality compiler. 2 A role in exascale? Current/Future HPC vendors are already involved (plus many others)... Apple + Google Intel (Many millions invested annually) + many others (Qualcomm, Sony, Microsoft, Facebook, Ericcson, etc.) ARM LLVM IBM Cray NVIDIA (and PGI) Academia, Labs, etc. AMD 3 What is LLVM: LLVM is a multi-architecture infrastructure for constructing compilers and other toolchain components. LLVM is not a “low-level virtual machine”! LLVM IR Architecture-independent simplification Architecture-aware optimization (e.g. vectorization) Assembly printing, binary generation, or JIT execution Backends (Type legalization, instruction selection, register allocation, etc.) 4 What is Clang: LLVM IR Clang is a C++ frontend for LLVM... Code generation Parsing and C++ Source semantic analysis (C++14, C11, etc.) Static analysis ● For basic compilation, Clang works just like gcc – using clang instead of gcc, or clang++ instead of g++, in your makefile will likely “just work.” ● Clang has a scalable LTO, check out: https://clang.llvm.org/docs/ThinLTO.html 5 The core LLVM compiler-infrastructure components are one of the subprojects in the LLVM project. These components are also referred to as “LLVM.” 6 What About Flang? ● Started as a collaboration between DOE and NVIDIA/PGI. -
CUDA Flux: a Lightweight Instruction Profiler for CUDA Applications
CUDA Flux: A Lightweight Instruction Profiler for CUDA Applications Lorenz Braun Holger Froning¨ Institute of Computer Engineering Institute of Computer Engineering Heidelberg University Heidelberg University Heidelberg, Germany Heidelberg, Germany [email protected] [email protected] Abstract—GPUs are powerful, massively parallel processors, hardware, it lacks verbosity when it comes to analyzing the which require a vast amount of thread parallelism to keep their different types of instructions executed. This problem is aggra- thousands of execution units busy, and to tolerate latency when vated by the recently frequent introduction of new instructions, accessing its high-throughput memory system. Understanding the behavior of massively threaded GPU programs can be for instance floating point operations with reduced precision difficult, even though recent GPUs provide an abundance of or special function operations including Tensor Cores for ma- hardware performance counters, which collect statistics about chine learning. Similarly, information on the use of vectorized certain events. Profiling tools that assist the user in such analysis operations is often lost. Characterizing workloads on PTX for their GPUs, like NVIDIA’s nvprof and cupti, are state-of- level is desirable as PTX allows us to determine the exact types the-art. However, instrumentation based on reading hardware performance counters can be slow, in particular when the number of the instructions a kernel executes. On the contrary, nvprof of metrics is large. Furthermore, the results can be inaccurate as metrics are based on a lower-level representation called SASS. instructions are grouped to match the available set of hardware Still, even if there was an exact mapping, some information on counters. -
Implementing Continuation Based Language in LLVM and Clang
Implementing Continuation based language in LLVM and Clang Kaito TOKUMORI Shinji KONO University of the Ryukyus University of the Ryukyus Email: [email protected] Email: [email protected] Abstract—The programming paradigm which use data seg- 1 __code f(Allocate allocate){ 2 allocate.size = 0; ments and code segments are proposed. This paradigm uses 3 goto g(allocate); Continuation based C (CbC), which a slight modified C language. 4 } 5 Code segments are units of calculation and Data segments are 6 // data segment definition sets of typed data. We use these segments as units of computation 7 // (generated automatically) 8 union Data { and meta computation. In this paper we show the implementation 9 struct Allocate { of CbC on LLVM and Clang 3.7. 10 long size; 11 } allocate; 12 }; I. A PRACTICAL CONTINUATION BASED LANGUAGE TABLE I The proposed units of programming are named code seg- CBCEXAMPLE ments and data segments. Code segments are units of calcu- lation which have no state. Data segments are sets of typed data. Code segments are connected to data segments by a meta data segment called a context. After the execution of a code have input data segments and output data segments. Data segment and its context, the next code segment (continuation) segments have two kind of dependency with code segments. is executed. First, Code segments access the contents of data segments Continuation based C (CbC) [1], hereafter referred to as using field names. So data segments should have the named CbC, is a slight modified C which supports code segments. -
Dynamic Extension of Typed Functional Languages
Dynamic Extension of Typed Functional Languages Don Stewart PhD Dissertation School of Computer Science and Engineering University of New South Wales 2010 Supervisor: Assoc. Prof. Manuel M. T. Chakravarty Co-supervisor: Dr. Gabriele Keller Abstract We present a solution to the problem of dynamic extension in statically typed functional languages with type erasure. The presented solution re- tains the benefits of static checking, including type safety, aggressive op- timizations, and native code compilation of components, while allowing extensibility of programs at runtime. Our approach is based on a framework for dynamic extension in a stat- ically typed setting, combining dynamic linking, runtime type checking, first class modules and code hot swapping. We show that this framework is sufficient to allow a broad class of dynamic extension capabilities in any statically typed functional language with type erasure semantics. Uniquely, we employ the full compile-time type system to perform run- time type checking of dynamic components, and emphasize the use of na- tive code extension to ensure that the performance benefits of static typing are retained in a dynamic environment. We also develop the concept of fully dynamic software architectures, where the static core is minimal and all code is hot swappable. Benefits of the approach include hot swappable code and sophisticated application extension via embedded domain specific languages. We instantiate the concepts of the framework via a full implementation in the Haskell programming language: providing rich mechanisms for dy- namic linking, loading, hot swapping, and runtime type checking in Haskell for the first time. We demonstrate the feasibility of this architecture through a number of novel applications: an extensible text editor; a plugin-based network chat bot; a simulator for polymer chemistry; and xmonad, an ex- tensible window manager. -
What I Wish I Knew When Learning Haskell
What I Wish I Knew When Learning Haskell Stephen Diehl 2 Version This is the fifth major draft of this document since 2009. All versions of this text are freely available onmywebsite: 1. HTML Version http://dev.stephendiehl.com/hask/index.html 2. PDF Version http://dev.stephendiehl.com/hask/tutorial.pdf 3. EPUB Version http://dev.stephendiehl.com/hask/tutorial.epub 4. Kindle Version http://dev.stephendiehl.com/hask/tutorial.mobi Pull requests are always accepted for fixes and additional content. The only way this document will stayupto date and accurate through the kindness of readers like you and community patches and pull requests on Github. https://github.com/sdiehl/wiwinwlh Publish Date: March 3, 2020 Git Commit: 77482103ff953a8f189a050c4271919846a56612 Author This text is authored by Stephen Diehl. 1. Web: www.stephendiehl.com 2. Twitter: https://twitter.com/smdiehl 3. Github: https://github.com/sdiehl Special thanks to Erik Aker for copyediting assistance. Copyright © 20092020 Stephen Diehl This code included in the text is dedicated to the public domain. You can copy, modify, distribute and perform thecode, even for commercial purposes, all without asking permission. You may distribute this text in its full form freely, but may not reauthor or sublicense this work. Any reproductions of major portions of the text must include attribution. The software is provided ”as is”, without warranty of any kind, express or implied, including But not limitedtothe warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall the authorsor copyright holders be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, Arising from, out of or in connection with the software or the use or other dealings in the software. -
The Glasgow Haskell Compiler User's Guide, Version 4.08
The Glasgow Haskell Compiler User's Guide, Version 4.08 The GHC Team The Glasgow Haskell Compiler User's Guide, Version 4.08 by The GHC Team Table of Contents The Glasgow Haskell Compiler License ........................................................................................... 9 1. Introduction to GHC ....................................................................................................................10 1.1. The (batch) compilation system components.....................................................................10 1.2. What really happens when I “compile” a Haskell program? .............................................11 1.3. Meta-information: Web sites, mailing lists, etc. ................................................................11 1.4. GHC version numbering policy .........................................................................................12 1.5. Release notes for version 4.08 (July 2000) ........................................................................13 1.5.1. User-visible compiler changes...............................................................................13 1.5.2. User-visible library changes ..................................................................................14 1.5.3. Internal changes.....................................................................................................14 2. Installing from binary distributions............................................................................................16 2.1. Installing on Unix-a-likes...................................................................................................16 -
The Glorious Glasgow Haskell Compilation System User's Guide
The Glorious Glasgow Haskell Compilation System User’s Guide, Version 7.8.2 i The Glorious Glasgow Haskell Compilation System User’s Guide, Version 7.8.2 The Glorious Glasgow Haskell Compilation System User’s Guide, Version 7.8.2 ii COLLABORATORS TITLE : The Glorious Glasgow Haskell Compilation System User’s Guide, Version 7.8.2 ACTION NAME DATE SIGNATURE WRITTEN BY The GHC Team April 11, 2014 REVISION HISTORY NUMBER DATE DESCRIPTION NAME The Glorious Glasgow Haskell Compilation System User’s Guide, Version 7.8.2 iii Contents 1 Introduction to GHC 1 1.1 Obtaining GHC . .1 1.2 Meta-information: Web sites, mailing lists, etc. .1 1.3 Reporting bugs in GHC . .2 1.4 GHC version numbering policy . .2 1.5 Release notes for version 7.8.1 . .3 1.5.1 Highlights . .3 1.5.2 Full details . .5 1.5.2.1 Language . .5 1.5.2.2 Compiler . .5 1.5.2.3 GHCi . .6 1.5.2.4 Template Haskell . .6 1.5.2.5 Runtime system . .6 1.5.2.6 Build system . .6 1.5.3 Libraries . .6 1.5.3.1 array . .6 1.5.3.2 base . .7 1.5.3.3 bin-package-db . .7 1.5.3.4 binary . .7 1.5.3.5 bytestring . .7 1.5.3.6 Cabal . .8 1.5.3.7 containers . .8 1.5.3.8 deepseq . .8 1.5.3.9 directory . .8 1.5.3.10 filepath . .8 1.5.3.11 ghc-prim . .8 1.5.3.12 haskell98 . -
Code Transformation and Analysis Using Clang and LLVM Static and Dynamic Analysis
Code transformation and analysis using Clang and LLVM Static and Dynamic Analysis Hal Finkel1 and G´abor Horv´ath2 Computer Science Summer School 2017 1 Argonne National Laboratory 2 Ericsson and E¨otv¨osLor´adUniversity Table of contents 1. Introduction 2. Static Analysis with Clang 3. Instrumentation and More 1 Introduction Space of Techniques During this set of lectures we'll cover a space of techniques for the analysis and transformation of code using LLVM. Each of these techniques have overlapping areas of applicability: Static Analysis LLVM Instrumentation Source Transformation 2 Space of Techniques When to use source-to-source transformation: • When you need to use the instrumented code with multiple compilers. • When you intend for the instrumentation to become a permanent part of the code base. You'll end up being concerned with the textual formatting of the instrumentation if humans also need to maintain or enhance this same code. 3 Space of Techniques When to use Clang's static analysis: • When the analysis can be performed on an AST representation. • When you'd like to maintain a strong connection to the original source code. • When false negatives are acceptable (i.e. it is okay if you miss problems). https://clang-analyzer.llvm.org/ 4 Space of Techniques When to use IR instrumentation: • When the necessary conditions can be (or can only be) detected at runtime (often in conjunction with a specialized runtime library). • When you require stronger coverage guarantees than static analysis. • When you'd like to reduce the cost of the instrumentation by running optimizations before the instrumentation is inserted, after the instrumentation is inserted, or both. -
Sham: a DSL for Fast Dsls
Sham: A DSL for Fast DSLs Rajan Waliaa, Chung-chieh Shana, and Sam Tobin-Hochstadta a Indiana University, Bloomington, United States Abstract Domain-specific languages (DSLs) are touted as both easy to embed in programs and easy to optimize. Yet these goals are often in tension. Embedded or internal DSLs fit naturally with a host language, while inheriting the host’s performance characteristics. External DSLs can use external optimizers and languages but sit apart from the host. We present Sham, a toolkit designed to enable internal DSLs with high performance. Sham provides a domain-specific language (embedded in Racket) for implementing other high-performance DSLs, with transparent compilation to assembly code at runtime. Sham is well suited as both a compilation target for other embedded DSLs and for transparently replacing DSL support code with faster versions. Sham provides seamless inter-operation with its host language without requiring any additional effort from its users. Sham also provides a framework for defining language syntax which implements Sham’s own language interface as well. We validate Sham’s design on a series of case studies, ranging from Krishnamurthi’s classic automata DSL to a sound synthesis DSL and a probabilistic programming language. All of these are existing DSLs where we replaced the backend using Sham, resulting in major performance gains. We present an example-driven description of how Sham can smoothly enhance an existing DSL into a high-performance one. When compared to existing approaches for implementing high-performance DSLs, Sham’s design aims for both simplicity and programmer control. This makes it easier to port our techniques to other languages and frameworks, or borrow Sham’s innovations “à la carte” without adopting the whole approach. -
Introduction to LLVM (Low Level Virtual Machine)
Introduction to LLVM (Low Level Virtual Machine) Fons Rademakers CERN What is the LLVM Project? • Collection of compiler technology components ■ Not a traditional “Virtual Machine” ■ Language independent optimizer and code generator ■ LLVM-GCC front-end ■ Clang front-end ■ Just-In-Time compiler • Open source project with many contributors ■ Industry, research groups, individuals ■ See http://llvm.org/Users.html • Everything is Open Source, and BSD Licensed! (except GCC) • For more see: http://llvm.org/ 2 LLVM as an Open-Source Project • Abridged project history ■ Dec. 2000: Started as a research project at University of Illinois ■ Oct. 2003: Open Source LLVM 1.0 release ■ June 2005: Apple hires project lead Chris Lattner ■ June 2008: LLVM GCC 4.2 ships in Apple Xcode 3.1 ■ Feb. 2009: LLVM 2.5 3 Why New Compilers? • Existing Open Source C Compilers have Stagnated! • How? ■ Based on decades old code generation technology ■ No modern techniques like cross-file optimization and JIT codegen ■ Aging code bases: difficult to learn, hard to change substantially ■ Can’t be reused in other applications ■ Keep getting slower with every release 4 LLVM Vision and Approach • Build a set of modular compiler components: ■ Reduces the time & cost to construct a particular compiler ■ Components are shared across different compilers ■ Allows choice of the right component for the job ■ Focus on compile and execution times ■ All components written in C++, ported to many platforms 5 First Product - LLVM-GCC 4.2 • C, C++, Objective C, Objective C++, Ada and Fortran • Same GCC command line options • Supports almost all GCC language features and extensions • Supports many targets, including X86, X86-64, PowerPC, etc.