226 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
Parallel discrete event simulation: A shared memory approach
With traditional event list techniques, evaluating a detailed discrete event simulation model can often require hours or even days of computation time. Parallel simulation mimics the interacting servers and queues of a real system by assigning each simulated entity to a processor. By eliminating the event list and maintaining only sufficient synchronization to insure causality, parallel simulation can potentially provide speedups that are linear in the number of processors. A set of shared memory experiments is presented using the Chandy-Misra distributed simulation algorithm to simulate networks of queues. Parameters include queueing network topology and routing probabilities, number of processors, and assignment of network nodes to processors. These experiments show that Chandy-Misra distributed simulation is a questionable alternative to sequential simulation of most queueing network models
07341 Abstracts Collection -- Code Instrumentation and Modeling for Parallel Performance Analysis
From 20th to 24th August 2007, the Dagstuhl Seminar 07341 ``Code Instrumentation and Modeling for Parallel Performance Analysis\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
The ISIS project: Fault-tolerance in large distributed systems
The semi-annual status report covers activities of the ISIS project during the second half of 1989. The project had several independent objectives: (1) At the level of the ISIS Toolkit, ISIS release V2.0 was completed, containing bypass communication protocols. Performance of the system is greatly enhanced by this change, but the initial software release is limited in some respects. (2) The Meta project focused on the definition of the Lomita programming language for specifying rules that monitor sensors for conditions of interest and triggering appropriate reactions. This design was completed, and implementation of Lomita is underway on the Meta 2.0 platform. (3) The Deceit file system effort completed a prototype. It is planned to make Deceit available for use in two hospital information systems. (4) A long-haul communication subsystem project was completed and can be used as part of ISIS. This effort resulted in tools for linking ISIS systems on different LANs together over long-haul communications lines. (5) Magic Lantern, a graphical tool for building application monitoring and control interfaces, is included as part of the general ISIS releases
Enabling Program Analysis Through Deterministic Replay and Optimistic Hybrid Analysis
As software continues to evolve, software systems increase in complexity. With software systems composed of many distinct but interacting components, today’s system programmers, users, and administrators find themselves requiring automated ways to find, understand, and handle system mis-behavior. Recent information breaches such as the Equifax breach of 2017, and the Heartbleed vulnerability of 2014 show the need to understand and debug prior states of computer systems.
In this thesis I focus on enabling practical entire-system retroactive analysis, allowing programmers, users, and system administrators to diagnose and understand the impact of these devastating mishaps. I focus primarly on two techniques. First, I discuss a novel deterministic record and replay system which enables fast, practical recollection of entire systems of computer state. Second, I discuss optimistic hybrid analysis, a novel optimization
method capable of dramatically accelerating retroactive program analysis.
Record and replay systems greatly aid in solving a variety of problems, such as fault tolerance, forensic analysis, and information providence. These solutions, however, assume ubiquitous recording of any application which may have a problem. Current record and replay systems are forced to trade-off between disk space and replay speed. This trade-off has historically made it impractical to both record and replay large histories of system level computation. I present Arnold, a novel record and replay system which efficiently records years of computation on a commodity hard-drive, and can efficiently replay any recorded information. Arnold combines caching with a unique process-group granularity
of recording to produce both small, and quickly recalled recordings. My experiments show that under a desktop workload, Arnold could store 4 years of computation on a commodity 4TB hard drive.
Dynamic analysis is used to retroactively identify and address many forms of system mis-behaviors including: programming errors, data-races, private information leakage, and memory errors. Unfortunately, the runtime overhead of dynamic analysis has precluded its adoption in many instances. I present a new dynamic analysis methodology called optimistic hybrid analysis (OHA). OHA uses knowledge of the past to predict program behaviors in the future. These predictions, or likely invariants are speculatively assumed true by a static analysis. This creates a static analysis which can be far more accurate than
its traditional counterpart. Once this predicated static analysis is created, it is speculatively used to optimize a final dynamic analysis, creating a far more efficient dynamic analysis than otherwise possible. I demonstrate the effectiveness of OHA by creating an optimistic hybrid backward slicer, OptSlice, and optimistic data-race detector OptFT. OptSlice and OptFT are just as accurate as their traditional hybrid counterparts, but run on average 8.3x
and 1.6x faster respectively.
In this thesis I demonstrate that Arnold’s ability to record and replay entire computer systems, combined with optimistic hybrid analysis’s ability to quickly analyze prior computation, enable a practical and useful entire system retroactive analysis that has been previously unrealized.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144052/1/ddevec_1.pd
OSCAR. A Noise Injection Framework for Testing Concurrent Software
“Moore’s Law” is a well-known observable phenomenon in computer science that describes a
visible yearly pattern in processor’s die increase. Even though it has held true for the last 57
years, thermal limitations on how much a processor’s core frequencies can be increased, have
led to physical limitations to their performance scaling. The industry has since then shifted
towards multicore architectures, which offer much better and scalable performance, while in
turn forcing programmers to adopt the concurrent programming paradigm when designing new
software, if they wish to make use of this added performance. The use of this paradigm comes
with the unfortunate downside of the sudden appearance of a plethora of additional errors in
their programs, stemming directly from their (poor) use of concurrency techniques.
Furthermore, these concurrent programs themselves are notoriously hard to design and to
verify their correctness, with researchers continuously developing new, more effective and effi-
cient methods of doing so. Noise injection, the theme of this dissertation, is one such method. It
relies on the “probe effect” — the observable shift in the behaviour of concurrent programs upon
the introduction of noise into their routines. The abandonment of ConTest, a popular proprietary
and closed-source noise injection framework, for testing concurrent software written using the
Java programming language, has left a void in the availability of noise injection frameworks for
this programming language.
To mitigate this void, this dissertation proposes OSCAR — a novel open-source noise injection
framework for the Java programming language, relying on static bytecode instrumentation for
injecting noise. OSCAR will provide a free and well-documented noise injection tool for research,
pedagogical and industry usage. Additionally, we propose a novel taxonomy for categorizing new
and existing noise injection heuristics, together with a new method for generating and analysing
concurrent software traces, based on string comparison metrics.
After noising programs from the IBM Concurrent Benchmark with different heuristics, we
observed that OSCAR is highly effective in increasing the coverage of the interleaving space, and
that the different heuristics provide diverse trade-offs on the cost and benefit (time/coverage) of
the noise injection process.Resumo
A “Lei de Moore” é um fenómeno, bem conhecido na área das ciências da computação, que
descreve um padrĂŁo evidente no aumento anual da densidade de transĂstores num processador.
Mesmo mantendo-se válido nos últimos 57 anos, o aumento do desempenho dos processadores
continua garrotado pelas limitações térmicas inerentes `a subida da sua frequência de funciona-
mento. Desde entĂŁo, a industria transitou para arquiteturas multi nĂşcleo, com significativamente
melhor e mais escalável desempenho, mas obrigando os programadores a adotar o paradigma
de programação concorrente ao desenhar os seus novos programas, para poderem aproveitar o
desempenho adicional que advém do seu uso. O uso deste paradigma, no entanto, traz consigo,
por consequência, a introdução de uma panóplia de novos erros nos programas, decorrentes
diretamente da utilização (inadequada) de técnicas de programação concorrente.
Adicionalmente, estes programas concorrentes sĂŁo conhecidos por serem consideravelmente
mais difĂceis de desenhar e de validar, quanto ao seu correto funcionamento, incentivando investi-
gadores ao desenvolvimento de novos métodos mais eficientes e eficazes de o fazerem. A injeção
de ruĂdo, o tema principal desta dissertação, Ă© um destes mĂ©todos. Esta baseia-se no “efeito sonda”
(do inglês “probe effect”) — caracterizado por uma mudança de comportamento observável em
programas concorrentes, ao terem ruĂdo introduzido nas suas rotinas. Com o abandono do Con-
Test, uma framework popular, proprietária e de código fechado, de análise dinâmica de programas
concorrentes atravĂ©s de injecção de ruĂdo, escritos com recurso `a linguagem de programação Java,
viu-se surgir um vazio na oferta de framework de injeção de ruĂdo, para esta mesma linguagem.
Para mitigar este vazio, esta dissertação propõe o OSCAR — uma nova framework de injeção de
ruĂdo, de cĂłdigo-aberto, para a linguagem de programação Java, que utiliza manipulação estática
de bytecode para realizar a introdução de ruĂdo. O OSCAR pretende oferecer uma ferramenta
livre e bem documentada de injeção de ruĂdo para fins de investigação, pedagĂłgicos ou atĂ© para
a indústria. Adicionalmente, a dissertação propõe uma nova taxonomia para categorizar os dife-
rentes tipos de heurĂsticas de injecção de ruĂdos novos e existentes, juntamente com um mĂ©todo
para gerar e analisar traces de programas concorrentes, com base em métricas de comparação de
strings.
ApĂłs inserir ruĂdo em programas do IBM Concurrent Benchmark, com diversas heurĂsticas, ob-
servámos que o OSCAR consegue aumentar significativamente a dimensĂŁo da cobertura do espaço de estados de programas concorrentes. Adicionalmente, verificou-se que diferentes heurĂsticas
produzem um leque variado de prós e contras, especialmente em termos de eficácia versus
eficiĂŞncia
Capturing High-level Nondeterminism in Concurrent Programs for Practical Concurrency Model Agnostic Record and Replay
With concurrency being integral to most software systems, developers combine high-level concurrency models in the same application to tackle each problem with appropriate abstractions. While languages and libraries offer a wide range of concurrency models, debugging support for applications that combine them has not yet gained much attention. Record & replay aids debugging by deterministically reproducing recorded bugs, but is typically designed for a single concurrency model only. This paper proposes a practical concurrency-model-agnostic record & replay approach for multi-paradigm concurrent programs, i.e. applications that combine concurrency models. Our approach traces high-level non- deterministic events by using a uniform model-agnostic trace format and infrastructure. This enables ordering- based record & replay support for a wide range of concurrency models, and thereby enables debugging of applications that combine them. In addition, it allows language implementors to add new concurrency mod- els and reuse the model-agnostic record & replay support. We argue that a concurrency-model-agnostic record & replay is practical and enables advanced debugging support for a wide range of concurrency models. The evaluation shows that our approach is expressive and flexible enough to support record & replay of applications using threads & locks, communicating event loops, communicating sequential processes, software transactional memory and combinations of those concurrency models. For the actor model, we reach recording performance competitive with an optimized special-purpose record & replay solution. The average recording overhead on the Savina actor benchmark suite is 10% (min. 0%, max. 23%). The performance for other concurrency models and combinations thereof is at a similar level. We believe our concurrency-model-agnostic approach helps developers of applications that mix and match concurrency models. We hope that this substrate inspires new tools and languages making building and maintaining of multi-paradigm concurrent applications simpler and safer
Autonomic State Management for Optimistic Simulation Platforms
We present the design and implementation of an autonomic state manager (ASM) tailored for integration within optimistic parallel discrete event simulation (PDES) environments based on the C programming language and the executable and linkable format (ELF), and developed for execution on x8664 architectures. With ASM, the state of any logical process (LP), namely the individual (concurrent) simulation unit being part of the simulation model, is allowed to be scattered on dynamically allocated memory chunks managed via standard API (e.g., malloc/free). Also, the application programmer is not required to provide any serialization/deserialization module in order to take a checkpoint of the LP state, or to restore it in case a causality error occurs during the optimistic run, or to provide indications on which portions of the state are updated by event processing, so to allow incremental checkpointing. All these tasks are handled by ASM in a fully transparent manner via (A) runtime identification (with chunk-level granularity) of the memory map associated with the LP state, and (B) runtime tracking of the memory updates occurring within chunks belonging to the dynamic memory map. The co-existence of the incremental and non-incremental log/restore modes is achieved via dual versions of the same application code, transparently generated by ASM via compile/link time facilities. Also, the dynamic selection of the best suited log/restore mode is actuated by ASM on the basis of an innovative modeling/optimization approach which takes into account stability of each operating mode with respect to variations of the model/environmental execution parameters
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