2,530 research outputs found
Deductive Verification of Unmodified Linux Kernel Library Functions
This paper presents results from the development and evaluation of a
deductive verification benchmark consisting of 26 unmodified Linux kernel
library functions implementing conventional memory and string operations. The
formal contract of the functions was extracted from their source code and was
represented in the form of preconditions and postconditions. The correctness of
23 functions was completely proved using AstraVer toolset, although success for
11 functions was achieved using 2 new specification language constructs.
Another 2 functions were proved after a minor modification of their source
code, while the final one cannot be completely proved using the existing memory
model. The benchmark can be used for the testing and evaluation of deductive
verification tools and as a starting point for verifying other parts of the
Linux kernel.Comment: 18 pages, 2 tables, 6 listings. Accepted to ISoLA 2018 conference.
Evaluating Tools for Software Verification trac
Holistic debugging - enabling instruction set simulation for software quality assurance
We present holistic debugging, a novel method for observing execution of complex and distributed software. It builds on an instruction set simulator, which provides reproducible experiments and non-intrusive probing of state in a distributed system. Instruction set simulators, however, only provide low-level information, so a holistic debugger contains a translation framework that maps this information to higher abstraction level observation tools, such as source code debuggers. We have created Nornir, a proof-of-concept holistic debugger, built on the simulator Simics. For each observed process in the simulated system, Nornir creates an abstraction translation stack, with virtual machine translators that map machine-level storage contents (e.g. physical memory, registers) provided by Simics, to application-level data (e.g. virtual memory contents) by parsing the data structures of operating systems and virtual machines. Nornir includes a modified version of the GNU debugger (GDB), which supports non-intrusive symbolic debugging of distributed applications. Nornir's main interface is a debugger shepherd, a programmable interface that controls multiple debuggers, and allows users to coherently inspect the entire state of heterogeneous, distributed applications. It provides a robust observation platform for construction of new observation tools
Design and Evaluation of a Collective IO Model for Loosely Coupled Petascale Programming
Loosely coupled programming is a powerful paradigm for rapidly creating
higher-level applications from scientific programs on petascale systems,
typically using scripting languages. This paradigm is a form of many-task
computing (MTC) which focuses on the passing of data between programs as
ordinary files rather than messages. While it has the significant benefits of
decoupling producer and consumer and allowing existing application programs to
be executed in parallel with no recoding, its typical implementation using
shared file systems places a high performance burden on the overall system and
on the user who will analyze and consume the downstream data. Previous efforts
have achieved great speedups with loosely coupled programs, but have done so
with careful manual tuning of all shared file system access. In this work, we
evaluate a prototype collective IO model for file-based MTC. The model enables
efficient and easy distribution of input data files to computing nodes and
gathering of output results from them. It eliminates the need for such manual
tuning and makes the programming of large-scale clusters using a loosely
coupled model easier. Our approach, inspired by in-memory approaches to
collective operations for parallel programming, builds on fast local file
systems to provide high-speed local file caches for parallel scripts, uses a
broadcast approach to handle distribution of common input data, and uses
efficient scatter/gather and caching techniques for input and output. We
describe the design of the prototype model, its implementation on the Blue
Gene/P supercomputer, and present preliminary measurements of its performance
on synthetic benchmarks and on a large-scale molecular dynamics application.Comment: IEEE Many-Task Computing on Grids and Supercomputers (MTAGS08) 200
Towards Loosely-Coupled Programming on Petascale Systems
We have extended the Falkon lightweight task execution framework to make
loosely coupled programming on petascale systems a practical and useful
programming model. This work studies and measures the performance factors
involved in applying this approach to enable the use of petascale systems by a
broader user community, and with greater ease. Our work enables the execution
of highly parallel computations composed of loosely coupled serial jobs with no
modifications to the respective applications. This approach allows a new-and
potentially far larger-class of applications to leverage petascale systems,
such as the IBM Blue Gene/P supercomputer. We present the challenges of I/O
performance encountered in making this model practical, and show results using
both microbenchmarks and real applications from two domains: economic energy
modeling and molecular dynamics. Our benchmarks show that we can scale up to
160K processor-cores with high efficiency, and can achieve sustained execution
rates of thousands of tasks per second.Comment: IEEE/ACM International Conference for High Performance Computing,
Networking, Storage and Analysis (SuperComputing/SC) 200
Connecting the World of Embedded Mobiles: The RIOT Approach to Ubiquitous Networking for the Internet of Things
The Internet of Things (IoT) is rapidly evolving based on low-power compliant
protocol standards that extend the Internet into the embedded world. Pioneering
implementations have proven it is feasible to inter-network very constrained
devices, but had to rely on peculiar cross-layered designs and offer a
minimalistic set of features. In the long run, however, professional use and
massive deployment of IoT devices require full-featured, cleanly composed, and
flexible network stacks.
This paper introduces the networking architecture that turns RIOT into a
powerful IoT system, to enable low-power wireless scenarios. RIOT networking
offers (i) a modular architecture with generic interfaces for plugging in
drivers, protocols, or entire stacks, (ii) support for multiple heterogeneous
interfaces and stacks that can concurrently operate, and (iii) GNRC, its
cleanly layered, recursively composed default network stack. We contribute an
in-depth analysis of the communication performance and resource efficiency of
RIOT, both on a micro-benchmarking level as well as by comparing IoT
communication across different platforms. Our findings show that, though it is
based on significantly different design trade-offs, the networking subsystem of
RIOT achieves a performance equivalent to that of Contiki and TinyOS, the two
operating systems which pioneered IoT software platforms
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