618 research outputs found
Strategic polymorphism requires just two combinators!
In previous work, we introduced the notion of functional strategies:
first-class generic functions that can traverse terms of any type while mixing
uniform and type-specific behaviour. Functional strategies transpose the notion
of term rewriting strategies (with coverage of traversal) to the functional
programming paradigm. Meanwhile, a number of Haskell-based models and
combinator suites were proposed to support generic programming with functional
strategies.
In the present paper, we provide a compact and matured reconstruction of
functional strategies. We capture strategic polymorphism by just two primitive
combinators. This is done without commitment to a specific functional language.
We analyse the design space for implementational models of functional
strategies. For completeness, we also provide an operational reference model
for implementing functional strategies (in Haskell). We demonstrate the
generality of our approach by reconstructing representative fragments of the
Strafunski library for functional strategies.Comment: A preliminary version of this paper was presented at IFL 2002, and
included in the informal preproceedings of the worksho
Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malware
While machine learning is vulnerable to adversarial examples, it still lacks
systematic procedures and tools for evaluating its security in different
application contexts. In this article, we discuss how to develop automated and
scalable security evaluations of machine learning using practical attacks,
reporting a use case on Windows malware detection
Achieving High-Performance the Functional Way: A Functional Pearl on Expressing High-Performance Optimizations as Rewrite Strategies
Optimizing programs to run efficiently on modern parallel hardware is hard but crucial for many applications. The predominantly used imperative languages - like C or OpenCL - force the programmer to intertwine the code describing functionality and optimizations. This results in a portability nightmare that is particularly problematic given the accelerating trend towards specialized hardware devices to further increase efficiency.
Many emerging DSLs used in performance demanding domains such as deep learning or high-performance image processing attempt to simplify or even fully automate the optimization process. Using a high-level - often functional - language, programmers focus on describing functionality in a declarative way. In some systems such as Halide or TVM, a separate schedule specifies how the program should be optimized. Unfortunately, these schedules are not written in well-defined programming languages. Instead, they are implemented as a set of ad-hoc predefined APIs that the compiler writers have exposed.
In this functional pearl, we show how to employ functional programming techniques to solve this challenge with elegance. We present two functional languages that work together - each addressing a separate concern. RISE is a functional language for expressing computations using well known functional data-parallel patterns. ELEVATE is a functional language for describing optimization strategies. A high-level RISE program is transformed into a low-level form using optimization strategies written in ELEVATE . From the rewritten low-level program high-performance parallel code is automatically generated. In contrast to existing high-performance domain-specific systems with scheduling APIs, in our approach programmers are not restricted to a set of built-in operations and optimizations but freely define their own computational patterns in RISE and optimization strategies in ELEVATE in a composable and reusable way. We show how our holistic functional approach achieves competitive performance with the state-of-the-art imperative systems Halide and TVM
The Sketch of a Polymorphic Symphony
In previous work, we have introduced functional strategies, that is,
first-class generic functions that can traverse into terms of any type while
mixing uniform and type-specific behaviour. In the present paper, we give a
detailed description of one particular Haskell-based model of functional
strategies. This model is characterised as follows. Firstly, we employ
first-class polymorphism as a form of second-order polymorphism as for the mere
types of functional strategies. Secondly, we use an encoding scheme of run-time
type case for mixing uniform and type-specific behaviour. Thirdly, we base all
traversal on a fundamental combinator for folding over constructor
applications.
Using this model, we capture common strategic traversal schemes in a highly
parameterised style. We study two original forms of parameterisation. Firstly,
we design parameters for the specific control-flow, data-flow and traversal
characteristics of more concrete traversal schemes. Secondly, we use
overloading to postpone commitment to a specific type scheme of traversal. The
resulting portfolio of traversal schemes can be regarded as a challenging
benchmark for setups for typed generic programming.
The way we develop the model and the suite of traversal schemes, it becomes
clear that parameterised + typed strategic programming is best viewed as a
potent combination of certain bits of parametric, intensional, polytypic, and
ad-hoc polymorphism
Eelco Visser: The Oregon Connection
This paper shares some memories of Eelco gathered over the past 25 years as a colleague and friend, and reflects on the nature of modern international collaborations
CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads
Index tuning, i.e., selecting the indexes appropriate for a workload, is a
crucial problem in database system tuning. In this paper, we solve index tuning
for large problem instances that are common in practice, e.g., thousands of
queries in the workload, thousands of candidate indexes and several hard and
soft constraints. Our work is the first to reveal that the index tuning problem
has a well structured space of solutions, and this space can be explored
efficiently with well known techniques from linear optimization. Experimental
results demonstrate that our approach outperforms state-of-the-art commercial
and research techniques by a significant margin (up to an order of magnitude).Comment: VLDB201
XT: a bundle of program transformation tools : system description
{sc xt bundles existing and newly developed program transformation libraries and tools into an open framework that supports component-based development of program transformations. We discuss the roles of {sc xt's constituents in the development process of program transformation tools, as well as some experiences with building program transformation systems with {sc xt. <pr
Quoted Staged Rewriting: A Practical Approach to Library-Defined Optimizations
Staging has proved a successful technique for programmatically removing code abstractions, thereby allowing for faster program execution while retaining a high-level interface for the programmer. Unfortunately, techniques based on staging suffer from a number of problems â ranging from practicalities to fundamental limitations â which have prevented their widespread adoption. We introduce Quoted Staged Rewriting (QSR), an approach that uses type-safe, pattern matching-enabled quasiquotes to define optimizations. The approach is âstagedâ in two ways: first, rewrite rules can execute arbitrary code during pattern matching and code reconstruction, leveraging the power and flexibility of staging; second, library designers can orchestrate the application of successive rewriting phases (stages). The advantages of using quasiquote-based rewriting are that library designers never have to deal directly with the intermediate representation (IR), and that it allows for non-intrusive optimizations â in contrast with staging, it is not necessary to adapt the entire library and user programs to accommodate optimizations. We show how Squid, a Scala macro-based framework, enables QSR and renders library-defined optimizations more practical than ever before: library designers write domain-specific optimizers that users invoke transparently on delimited portions of their code base. As a motivating example we describe an implementation of stream fusion (a well-known deforestation technique) that is both simpler and more powerful than the state of the art, and can readily be used by Scala programmers with no knowledge of metaprogramming
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