314,928 research outputs found
Formalizing the SSA-based Compiler for Verified Advanced Program Transformations
Compilers are not always correct due to the complexity of language semantics and transformation algorithms, the trade-offs between compilation speed and verifiability,etc.The bugs of compilers can undermine the source-level verification efforts (such as type systems, static analysis, and formal proofs) and produce target programs with different meaning from source programs. Researchers have used mechanized proof tools to implement verified compilers that are guaranteed to preserve program semantics and proved to be more robust than ad-hoc non-verified compilers.
The goal of the dissertation is to make a step towards verifying an industrial strength modern compiler--LLVM, which has a typed, SSA-based, and general-purpose intermediate representation, therefore allowing more advanced program transformations than existing approaches. The dissertation formally defines the sequential semantics of the LLVM intermediate representation with its type system, SSA properties, memory model, and operational semantics. To design and reason about program transformations in the LLVM IR, we provide tools for interacting with the LLVM infrastructure and metatheory for SSA properties, memory safety, dynamic semantics, and control-flow-graphs. Based on the tools and metatheory, the dissertation implements verified and extractable applications for LLVM that include an interpreter for the LLVM IR, a transformation for enforcing memory safety, translation validators for local optimizations, and verified SSA construction transformation.
This dissertation shows that formal models of SSA-based compiler intermediate representations can be used to verify low-level program transformations, thereby enabling the construction of high-assurance compiler passes
Compiler-Assisted Checkpointing of Parallel Codes: The Cetus and LLVM Experience
This is a post-peer-review, pre-copyedit version of an article published in International Journal of Parallel Programming. The final authenticated version is available online at: https://doi.org/10.1007/s10766-012-0231-8[Abstract] With the evolution of high-performance computing, parallel applications have developed an increasing necessity for fault tolerance, most commonly provided by checkpoint and restart techniques. Checkpointing tools are typically implemented at one of two different abstraction levels: at the system level or at the application level. The latter has become an interesting alternative due to its flexibility and the possibility of operating in different environments. However, application-level checkpointing tools often require the user to manually insert checkpoints in order to ensure that certain requirements are met (e.g. forcing checkpoints to be taken at the user code and not inside kernel routines). This paper examines the transformations required to enable automatic checkpointing of parallel applications in the CPPC application-level checkpointing framework. These transformations have been implemented on two very different compiler infrastructures: Cetus and LLVM. Cetus is a Java-based compiler infrastructure aiming to provide an easy to use and clean IR and API for program transformation. LLVM is a low-level, SSA-based toolchain. The fundamental differences of both approaches are analyzed from the structural, behavioral and performance perspectives.Galicia. ConsellerĂa de EconomĂa e Industria; 10PXIB105180PRMinisterio de Ciencia e InnovaciĂłn; TIN2010-1673
Change and Transformation in Asian Industrial Relations
Authors argue that industrial relations systems change due to shifts in the constraints facing those systems, and that the most salient constraints facing IR systems in Asia have shifted from those of maintaining labor peace and stability in the early stages of industrialization, to those of increasing both numerical and functional flexibility in the 1980s and 1990s. The evidence to sustain the argument is drawn from seven “representative” Asian IR systems: Japan, South Korea, Singapore, Malaysia, the Philippines, India, and China. They also distinguish between systems that have smoothly adapted (Singapore, Malaysia, and the Philippines) and systems that have fundamentally transformed (China and South Korea), and hypothesize about the reasons for this difference
Relay: A New IR for Machine Learning Frameworks
Machine learning powers diverse services in industry including search,
translation, recommendation systems, and security. The scale and importance of
these models require that they be efficient, expressive, and portable across an
array of heterogeneous hardware devices. These constraints are often at odds;
in order to better accommodate them we propose a new high-level intermediate
representation (IR) called Relay. Relay is being designed as a
purely-functional, statically-typed language with the goal of balancing
efficient compilation, expressiveness, and portability. We discuss the goals of
Relay and highlight its important design constraints. Our prototype is part of
the open source NNVM compiler framework, which powers Amazon's deep learning
framework MxNet
A Language and Hardware Independent Approach to Quantum-Classical Computing
Heterogeneous high-performance computing (HPC) systems offer novel
architectures which accelerate specific workloads through judicious use of
specialized coprocessors. A promising architectural approach for future
scientific computations is provided by heterogeneous HPC systems integrating
quantum processing units (QPUs). To this end, we present XACC (eXtreme-scale
ACCelerator) --- a programming model and software framework that enables
quantum acceleration within standard or HPC software workflows. XACC follows a
coprocessor machine model that is independent of the underlying quantum
computing hardware, thereby enabling quantum programs to be defined and
executed on a variety of QPUs types through a unified application programming
interface. Moreover, XACC defines a polymorphic low-level intermediate
representation, and an extensible compiler frontend that enables language
independent quantum programming, thus promoting integration and
interoperability across the quantum programming landscape. In this work we
define the software architecture enabling our hardware and language independent
approach, and demonstrate its usefulness across a range of quantum computing
models through illustrative examples involving the compilation and execution of
gate and annealing-based quantum programs
Sawja: Static Analysis Workshop for Java
Static analysis is a powerful technique for automatic verification of
programs but raises major engineering challenges when developing a full-fledged
analyzer for a realistic language such as Java. This paper describes the Sawja
library: a static analysis framework fully compliant with Java 6 which provides
OCaml modules for efficiently manipulating Java bytecode programs. We present
the main features of the library, including (i) efficient functional
data-structures for representing program with implicit sharing and lazy
parsing, (ii) an intermediate stack-less representation, and (iii) fast
computation and manipulation of complete programs
Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code
This paper introduces Tiramisu, a polyhedral framework designed to generate
high performance code for multiple platforms including multicores, GPUs, and
distributed machines. Tiramisu introduces a scheduling language with novel
extensions to explicitly manage the complexities that arise when targeting
these systems. The framework is designed for the areas of image processing,
stencils, linear algebra and deep learning. Tiramisu has two main features: it
relies on a flexible representation based on the polyhedral model and it has a
rich scheduling language allowing fine-grained control of optimizations.
Tiramisu uses a four-level intermediate representation that allows full
separation between the algorithms, loop transformations, data layouts, and
communication. This separation simplifies targeting multiple hardware
architectures with the same algorithm. We evaluate Tiramisu by writing a set of
image processing, deep learning, and linear algebra benchmarks and compare them
with state-of-the-art compilers and hand-tuned libraries. We show that Tiramisu
matches or outperforms existing compilers and libraries on different hardware
architectures, including multicore CPUs, GPUs, and distributed machines.Comment: arXiv admin note: substantial text overlap with arXiv:1803.0041
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