144,832 research outputs found
Distributed System Contract Monitoring
The use of behavioural contracts, to specify, regulate and verify systems, is
particularly relevant to runtime monitoring of distributed systems. System
distribution poses major challenges to contract monitoring, from
monitoring-induced information leaks to computation load balancing,
communication overheads and fault-tolerance. We present mDPi, a location-aware
process calculus, for reasoning about monitoring of distributed systems. We
define a family of Labelled Transition Systems for this calculus, which allow
formal reasoning about different monitoring strategies at different levels of
abstractions. We also illustrate the expressivity of the calculus by showing
how contracts in a simple contract language can be synthesised into different
mDPi monitors.Comment: In Proceedings FLACOS 2011, arXiv:1109.239
Scalable data abstractions for distributed parallel computations
The ability to express a program as a hierarchical composition of parts is an
essential tool in managing the complexity of software and a key abstraction
this provides is to separate the representation of data from the computation.
Many current parallel programming models use a shared memory model to provide
data abstraction but this doesn't scale well with large numbers of cores due to
non-determinism and access latency. This paper proposes a simple programming
model that allows scalable parallel programs to be expressed with distributed
representations of data and it provides the programmer with the flexibility to
employ shared or distributed styles of data-parallelism where applicable. It is
capable of an efficient implementation, and with the provision of a small set
of primitive capabilities in the hardware, it can be compiled to operate
directly on the hardware, in the same way stack-based allocation operates for
subroutines in sequential machines
Toward Sequentializing Overparallelized Protocol Code
In our ongoing work, we use constraint automata to compile protocol
specifications expressed as Reo connectors into efficient executable code,
e.g., in C. We have by now studied this automata based compilation approach
rather well, and have devised effective solutions to some of its problems.
Because our approach is based on constraint automata, the approach, its
problems, and our solutions are in fact useful and relevant well beyond the
specific case of compiling Reo. In this short paper, we identify and analyze
two such rather unexpected problems.Comment: In Proceedings ICE 2014, arXiv:1410.701
Enabling Cross-Event Optimization in Discrete-Event Simulation Through Compile-Time Event Batching
A discrete-event simulation (DES) involves the execution of a sequence of
event handlers dynamically scheduled at runtime. As a consequence, a priori
knowledge of the control flow of the overall simulation program is limited. In
particular, powerful optimizations supported by modern compilers can only be
applied on the scope of individual event handlers, which frequently involve
only a few lines of code. We propose a method that extends the scope for
compiler optimizations in discrete-event simulations by generating batches of
multiple events that are subjected to compiler optimizations as contiguous
procedures. A runtime mechanism executes suitable batches at negligible
overhead. Our method does not require any compiler extensions and introduces
only minor additional effort during model development. The feasibility and
potential performance gains of the approach are illustrated on the example of
an idealized proof-ofconcept model. We believe that the applicability of the
approach extends to general event-driven programs
Building Efficient Query Engines in a High-Level Language
Abstraction without regret refers to the vision of using high-level
programming languages for systems development without experiencing a negative
impact on performance. A database system designed according to this vision
offers both increased productivity and high performance, instead of sacrificing
the former for the latter as is the case with existing, monolithic
implementations that are hard to maintain and extend. In this article, we
realize this vision in the domain of analytical query processing. We present
LegoBase, a query engine written in the high-level language Scala. The key
technique to regain efficiency is to apply generative programming: LegoBase
performs source-to-source compilation and optimizes the entire query engine by
converting the high-level Scala code to specialized, low-level C code. We show
how generative programming allows to easily implement a wide spectrum of
optimizations, such as introducing data partitioning or switching from a row to
a column data layout, which are difficult to achieve with existing low-level
query compilers that handle only queries. We demonstrate that sufficiently
powerful abstractions are essential for dealing with the complexity of the
optimization effort, shielding developers from compiler internals and
decoupling individual optimizations from each other. We evaluate our approach
with the TPC-H benchmark and show that: (a) With all optimizations enabled,
LegoBase significantly outperforms a commercial database and an existing query
compiler. (b) Programmers need to provide just a few hundred lines of
high-level code for implementing the optimizations, instead of complicated
low-level code that is required by existing query compilation approaches. (c)
The compilation overhead is low compared to the overall execution time, thus
making our approach usable in practice for compiling query engines
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