100,179 research outputs found
Scalable discovery of hybrid process models in a cloud computing environment
Process descriptions are used to create products and deliver services. To lead better processes and services, the first step
is to learn a process model. Process discovery is such a technique which can automatically extract process models from event logs.
Although various discovery techniques have been proposed, they focus on either constructing formal models which are very powerful
but complex, or creating informal models which are intuitive but lack semantics. In this work, we introduce a novel method that returns
hybrid process models to bridge this gap. Moreover, to cope with today’s big event logs, we propose an efficient method, called f-HMD,
aims at scalable hybrid model discovery in a cloud computing environment. We present the detailed implementation of our approach
over the Spark framework, and our experimental results demonstrate that the proposed method is efficient and scalabl
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On the conditions for efficient interoperability with threads: An experience with PGAS languages using Cray communication domains
Today's high performance systems are typically built from shared memory nodes connected by a high speed network. That architecture, combined with the trend towards less memory per core, encourages programmers to use a mixture of message passing and multithreaded programming. Unfortunately, the advantages of using threads for in-node programming are hindered by their inability to efficiently communicate between nodes. In this work, we identify some of the performance problems that arise in such hybrid programming environments and characterize conditions needed to achieve high communication performance for multiple threads: addressability of targets, separability of communication paths, and full direct reachability to targets. Using the GASNet communication layer on the Cray XC30 as our experimental platform, we show how to satisfy these conditions. We also discuss how satisfying these conditions is influenced by the communication abstraction, implementation constraints, and the interconnect messaging capabilities. To evaluate these ideas, we compare the communication performance of a thread-based node runtime to a process-based runtime. Without our GASNet extensions, thread communication is significantly slower than processes - up to 21x slower. Once the implementation is modified to address each of our conditions, the two runtimes have comparable communication performance. This allows programmers to more easily mix models like OpenMP, CILK, or pthreads with a GASNet-based model like UPC, with the associated performance, convenience and interoperability advantages that come from using threads within a node. © 2014 ACM
CUP: Comprehensive User-Space Protection for C/C++
Memory corruption vulnerabilities in C/C++ applications enable attackers to
execute code, change data, and leak information. Current memory sanitizers do
no provide comprehensive coverage of a program's data. In particular, existing
tools focus primarily on heap allocations with limited support for stack
allocations and globals. Additionally, existing tools focus on the main
executable with limited support for system libraries. Further, they suffer from
both false positives and false negatives.
We present Comprehensive User-Space Protection for C/C++, CUP, an LLVM
sanitizer that provides complete spatial and probabilistic temporal memory
safety for C/C++ program on 64-bit architectures (with a prototype
implementation for x86_64). CUP uses a hybrid metadata scheme that supports all
program data including globals, heap, or stack and maintains the ABI. Compared
to existing approaches with the NIST Juliet test suite, CUP reduces false
negatives by 10x (0.1%) compared to the state of the art LLVM sanitizers, and
produces no false positives. CUP instruments all user-space code, including
libc and other system libraries, removing them from the trusted code base
A Parallel Mesh-Adaptive Framework for Hyperbolic Conservation Laws
We report on the development of a computational framework for the parallel,
mesh-adaptive solution of systems of hyperbolic conservation laws like the
time-dependent Euler equations in compressible gas dynamics or
Magneto-Hydrodynamics (MHD) and similar models in plasma physics. Local mesh
refinement is realized by the recursive bisection of grid blocks along each
spatial dimension, implemented numerical schemes include standard
finite-differences as well as shock-capturing central schemes, both in
connection with Runge-Kutta type integrators. Parallel execution is achieved
through a configurable hybrid of POSIX-multi-threading and MPI-distribution
with dynamic load balancing. One- two- and three-dimensional test computations
for the Euler equations have been carried out and show good parallel scaling
behavior. The Racoon framework is currently used to study the formation of
singularities in plasmas and fluids.Comment: late submissio
StocHy: automated verification and synthesis of stochastic processes
StocHy is a software tool for the quantitative analysis of discrete-time
stochastic hybrid systems (SHS). StocHy accepts a high-level description of
stochastic models and constructs an equivalent SHS model. The tool allows to
(i) simulate the SHS evolution over a given time horizon; and to automatically
construct formal abstractions of the SHS. Abstractions are then employed for
(ii) formal verification or (iii) control (policy, strategy) synthesis. StocHy
allows for modular modelling, and has separate simulation, verification and
synthesis engines, which are implemented as independent libraries. This allows
for libraries to be easily used and for extensions to be easily built. The tool
is implemented in C++ and employs manipulations based on vector calculus, the
use of sparse matrices, the symbolic construction of probabilistic kernels, and
multi-threading. Experiments show StocHy's markedly improved performance when
compared to existing abstraction-based approaches: in particular, StocHy beats
state-of-the-art tools in terms of precision (abstraction error) and
computational effort, and finally attains scalability to large-sized models (12
continuous dimensions). StocHy is available at www.gitlab.com/natchi92/StocHy
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