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Jurrien Stutterheim, Peter Achten, Rinus Plasmeijer: A Graphical
Tracer and Debugger for Designers, Programmers and End-Users of Task
Oriented Applications
In Task Oriented
Programming (TOP), tasks, as performed by both humans and computers, are
the core concept. TOP is offered by the iTask
system (iTasks) as a shallowly embedded Domain
Specific Language (DSL) in the pure functional programming language Clean.
In iTasks, arbitrary complex multi-user / multi-systems
collaborations over the internet can be defined.
From the
specification an application is generated which coordinates the work thus
described over the internet. The description is very concise: one can focus
on the description of the tasks to do and abstract from the many technical
details (e.g. user interface generation, code to run on the client or
server, client-server communication and synchronization) one commonly has
to define explicitly.
iTasks is used in industry for rapid prototyping in
complex sociotechnical domains such as command and control systems.
However, for non-technical industrial stakeholders or end-users of the system,
formal task (function) definitions as given in a pure functional language
are commonly too difficult to understand. They are more used to communicate
their ideas informally, using drawings and natural language, while TOP
programmers model tasks as functions in Clean.
To bridge the gap
we want to offer a simplified graphical view of an iTask
program that can be used to communicate ideas between designers and
programmers when developing an iTask application.
Furthermore we want to show graphically how tasks are evolving at run-time,
showing developers but also end-users what is actually happening, such that
they can react in a proper way to the situations that occur.
In this talk we
present the TONIC graphical tracer and debugger for iTasks.
It uses a special Clean compiler which generates additional information for
monad-like structures such as task definitions. This information is used to
show static blueprints: a simplified graphical representation of the tasks
being defined, a kind of parameterized flow graphs. When tasks are started
at run-time, the static blueprints are instantiated into dynamic
blueprints. These show which tasks
are actually being performed, which one are finished, or which still have
to be done. Arguments and results of tasks can be inspected, not only from
finished tasks but also from tasks one is currently working on. TONIC
enables designers, developers, and end-users to inspect the task
definitions and run-time behavior which can be
used to further improve the application.
Pieter Koopman and Rinus Plasmeijer:
A Shallow Embedded Type Safe Extendable DSL for the Arduino
In this paper we
describe a shallow embedded type safe extendable domain specific language
to program a microprocessor system. Our long term goal is to run parts of
Clean programs that require special input/output actions on such a
microprocessor.
We use a number of
new implementation techniques to make this domain specific language type
safe and extendable. It is type safe since ever program in the domain
specific language approved by the compiler of the host language is also
well type in the C-dialect of the microprocessor. The language is
extendable since we can add language constructs or views without touching
existing code. This makes it a successful solution for Wadler's
expression problem.
Adam Granicz: Functional, Reactive Web Programming in F#
In this lecture, we will take a look at the basics of functional
and reactive web programming through WebSharper –
a mature web development stack for F#, and it’s UI.Next library for
reactive DOM construction and dynamic dataflow. You will learn the theory behind similar
technologies, discover its advantages, and develop simple applications using the concepts
learned.
Tamás Kozsik: Parallelization by Refactoring
Refactoring is the process of restructuring, shaping or transforming a
program in order to improve its quality, to change its non-functional
properties, or to make it suitable to add a new feature. This activity
can be carried out by hand, or by using program transformation tools.
One possible application area of refactoring is the introduction of
parallelism into sequential programs. When parallelizing
industrial-scale software applications, a tool can provide invaluable
help in decision making as well as in the semi-automatic application of
refactoring transformations. Such a tool should offer guidance to its
user on what refactoring decisions are to be made, on where it is the
most fruitful to introduce parallelism, and on how to achieve the
desired program structure.
A modern, structured design and implementation approach for parallel
programming allows developers exploit a variety of high-level parallel
patterns to develop component-based applications that can be mapped to
the available hardware resources, and which may then be dynamically
re-mapped to meet application needs and hardware availability. This
tutorial presents tools and techniques that can (semi-)automatically
locate suitable pattern candidates in Erlang programs, and recommend
transformed versions of these pattern candidates that yield significant
speedup on a given parallel architecture.
Ivan Čukić:
Functional Reactive Programming for C++
Functional reactive programming is a discipline which tries to bring
cleaner and safer ways to develop asynchronous systems. It is suitable
for writing all kinds of event-based systems, or any type of system
where separate components need to be executed asynchronously, but
still need to communicate with each other. This includes web services
and distributed systems, but also regular consumer GUI applications.
Reactive programming creates abstractions on top of various low-level
approaches for asynchronous execution such as threads, actors,
callbacks and such. Those abstractions extend the commonly used
higher-order functional programming concepts to be applicable to
concurrent systems.
We will present a couple of reactive programming idioms with their
implementations and examples in the C++14 language, along with their
counter-parts in 'real' functional languages like Haskell. We will
also take a look at the evolution of C++, with a special attention
given to a few upcoming features planned for C++17.
Rainer Grimm: Programming in a Functional Style in Modern C++
C++ is a multi paradigm programming language. So the programmer may
choose and combine between structural, procedural, object oriented,
generic or functional features of C++ to solve his problem. Especially
the functional aspect of C++ with lambda functions, type inference and
the function std::bind and std::function has grown in modern C++ and is
still evolving with the next C++ standards.
This lecture gives an overview of the functional capabilities of modern
C++, compares this features with that of Haskell and shows, how you can
use them. In addition, the lecture tries to peek in the future and gives
an idea of what to come in the near future.
Clemens
Grelck: Single
Assignment C: From Functional Programming with Curly Brackets to High
Performance Computing
SAC (Single
Assignment C) is in several aspects a functional programming language out
of the ordinary. As the name suggests, SAC combines a C-like syntax (with
lots of curly brackets) with a state-free, purely functional semantics.
Originally motivated to ease adoption by programmers with an imperative
background, the choice offers surprising insights into what constitutes a
"typical" functional or a "typical" imperative language
construct.
Again on the exotic
side, SAC does not favour lists and trees, or more generally algebraic data
types, but puts all emphasis on multi-dimensional arrays as the primary
data structure. Based on a formal array calculus SAC supports declarative
array processing in the spirit of interpreted languages such as APL. Array
programming treats multidimensional arrays in a holistic way: functions map
potentially huge argument array values into result array values following a
call-by-value semantics and new array operations are defined by composition
of existing ones.
SAC is a
high-productivity language for application domains that deal with large
collections of data in a computationally intensive way. At the same time
SAC also is a high performance language competing with low-level imperative
languages through compilation technology. The abstract view on arrays
combined with the functional semantics support far-reaching program
transformations. A highly optimised runtime system takes care of automatic
memory management with an emphasis on immediate reuse. Last not least, the
SAC compiler exploits the state-free semantics of SAC and the data-parallel
nature of SAC programs for fully compiler-directed acceleration on
contemporary multi- and many-core architectures.
João Paulo Fernandes:
Keep Your Modular Programs Efficient Through
Deforestation
Many reasons
contribute to the beauty and effectiveness of functional programming.
In his 1990 paper
on Why Functional Programming Matters, John Hughes already synthesized
modularity as being one such fundamental reason. Back in 1990, Hughes
claimed that two characteristics of modern functional languages have
essential contributions to modularity: higher-order functions and lazy
evaluation. With higher-order functions we can explore generic program
schemes, or patterns, which can be reused and instantiated multiple times;
in a lazy setting we can search ideal candidates in a potentially infinite
search space (created lazily).
A modular setting
is particularly convenient for programmers: it eases the programming task
itself, encourages reuse, facilitates analysis and
debugging. But such a setting may also entail a drawback: as it encourages
a compositional style of programming where non-trivial solutions are
constructed composing simple functions, intermediate structures need to be
constructed to serve as connectors of such functions. And constructing,
traversing and destroying these data structures may degrade the performance
of the resulting implementations.
From a practical
perspective we, as programmers, would like to build solutions that are as
modular as possible, but without incurring on any performance penalties.
The purpose of this tutorial is to go through several state-of-the-art
transformational approaches for achieving this practical goal. We exploit
the equational reasoning that is offered by functional languages in order
to transform modular programs into deforested ones, i.e., into programs
that do not construct any intermediate structure.
The beauty of these
approaches is that, again, higher-order functions and laziness play an
essential role. Indeed, 1) the programs we are able to transform are first
expressed in terms of well known higher-order
program schemes and 2) some of the programs we derive from them rely on
higher-order functions or on lazy evaluation to be executed.
Lab materials:
(pdf) and (lhs)
Štefan Korečko: Functional Languages in Design of Coloured
Petri Nets Models
Coloured Petri nets
(CPN) represent a formal method that allows to create
sophisticated event-driven models. In addition, there exists a software
tool, called CPN Tools, which provides a support for creation, simulation
and state space-based verification of CPN models. An interesting feature of
CPN Tools is that is uses CPN ML, a slightly modified version of the
Standard ML functional language, for data manipulation. A full power of
Standard ML is at disposal and it is up to the modeller how extensively he
will use the language in his models. In this short tutorial we introduce
basic concepts of Coloured Petri nets and show how CPN ML fits into these
concepts. The participants will also have a chance to develop their own CPN
models.
Zoltán Porkoláb: Immutable Programming in C++
The C++ programming language is a multiparadigm language, with a rich set
of procedural, object-oriented, generative and (since C++11) functional
language elements. The language is also well-known about its capability to
map certain semantic features into the language syntax, therefore the
compiler can reason about them in compilation time. Const-correctness is
one of such aspects: the programmer can mark immutable components and the
compiler checks any potential violation scenarios and also can optimize
the code.
We will overview the immutable and the functional features of the modern
C++ language: the relaxed constexpr (since C++14), the generalized lambda
expressions (since C++14), usage of immutable containers. We will show how
to program in C++ in “functional style”, and when to use mutable. We also
discuss the fundamentals of C++ template metaprogramming – a pure
functional paradigm at compilation time.
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