41,657 research outputs found
Execution replay and debugging
As most parallel and distributed programs are internally non-deterministic --
consecutive runs with the same input might result in a different program flow
-- vanilla cyclic debugging techniques as such are useless. In order to use
cyclic debugging tools, we need a tool that records information about an
execution so that it can be replayed for debugging. Because recording
information interferes with the execution, we must limit the amount of
information and keep the processing of the information fast. This paper
contains a survey of existing execution replay techniques and tools.Comment: In M. Ducasse (ed), proceedings of the Fourth International Workshop
on Automated Debugging (AADebug 2000), August 2000, Munich. cs.SE/001003
Non-intrusive on-the-fly data race detection using execution replay
This paper presents a practical solution for detecting data races in parallel
programs. The solution consists of a combination of execution replay (RecPlay)
with automatic on-the-fly data race detection. This combination enables us to
perform the data race detection on an unaltered execution (almost no probe
effect). Furthermore, the usage of multilevel bitmaps and snooped matrix clocks
limits the amount of memory used. As the record phase of RecPlay is highly
efficient, there is no need to switch it off, hereby eliminating the
possibility of Heisenbugs because tracing can be left on all the time.Comment: In M. Ducasse (ed), proceedings of the Fourth International Workshop
on Automated Debugging (AAdebug 2000), August 2000, Munich. cs.SE/001003
Algorithm Diversity for Resilient Systems
Diversity can significantly increase the resilience of systems, by reducing
the prevalence of shared vulnerabilities and making vulnerabilities harder to
exploit. Work on software diversity for security typically creates variants of
a program using low-level code transformations. This paper is the first to
study algorithm diversity for resilience. We first describe how a method based
on high-level invariants and systematic incrementalization can be used to
create algorithm variants. Executing multiple variants in parallel and
comparing their outputs provides greater resilience than executing one variant.
To prevent different parallel schedules from causing variants' behaviors to
diverge, we present a synchronized execution algorithm for DistAlgo, an
extension of Python for high-level, precise, executable specifications of
distributed algorithms. We propose static and dynamic metrics for measuring
diversity. An experimental evaluation of algorithm diversity combined with
implementation-level diversity for several sequential algorithms and
distributed algorithms shows the benefits of algorithm diversity
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
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