Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

National Institute of Standards and Technology における超伝導研究及び生活

This survey covers rollback-recovery techniques that do not require special language constructs. In the first part of the survey we classify rollback-recovery protocols into checkpoint-based and log-based.Checkpoint-based protocols rely solely on checkpointing for system state restoration. Checkpointing can be coordinated, uncoordinated, or communication-induced. Log-based protocols combine checkpointing with logging of nondeterministic events, encoded in tuples called determinants. Depending on how determinants are logged, log-based protocols can be pessimistic, optimistic, or causal. Throughout the survey, we highlight the research issues that are at the core of rollback-recovery and present the solutions that currently address them. We also compare the performance of different rollback-recovery protocols with respect to a series of desirable properties and discuss the issues that arise in the practical implementations of these protocols.

/pdf/a-survey-of-rollback-recovery-protocols-in-message-passing-bnrpate93a.pdf

A survey of rollback-recovery protocols in message-passing systems

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[서평]「Computer Organization and Design, The Hardware/Software Interface」

DPDK (Data Plane Development Kit) is arguably today's most employed framework for software packet processing. Its impressive performance however comes at the cost of precious CPU resources, dedicated to continuously poll the NICs. To face this issue, this paper presents Metronome, an approach devised to replace the continuous DPDK polling with a sleep&wake intermittent mode. Metronome revolves around two main innovations. First, we design a microseconds time-scale sleep function, named hr_sleep(), which outperforms Linux' nanosleep() of more than one order of magnitude in terms of precision when running threads with common time-sharing priorities. Then, we design, model, and assess an efficient multi-thread operation which guarantees service continuity and improved robustness against preemptive thread executions, like in common CPU-sharing scenarios, meanwhile providing controlled latency and high polling efficiency by dynamically adapting to the measured traffic load.

https://dl.acm.org/doi/pdf/10.1145/3386367.3432730

Metronome: adaptive and precise intermittent packet retrieval in DPDK

This paper presents an analytical evaluation of the performance of adaptive wormhole routing in a two-dimensional torus. Our analysis focuses on minimal and fully adaptive wormhole routing that allows a message to use any shortest path between source and destination. A validation of the analysis through simulation is presented to demonstrate the accuracy of the obtained results. Finally, we remark that no theoretical limitation prevents the extension of our analytical approach to the evaluation of the performance of adaptive wormhole routing in hypercubes or other symmetric topologies with wrap-around connections.

Performance analysis of adaptive wormhole routing in a two-dimensional torus

Communication-induced checkpointing algorithms require cooperating processes, which take checkpoints at their own pace, to take some forced checkpoints in order to guarantee domino-freeness. In this paper we present a checkpointing-recovery scheme which reduces the number of forced checkpoints, compared to previous solutions, while piggybacking, on each message, only three integers as control information. This is achieved by using information about the history of a process and an equivalence relation between local checkpoints that we introduce in this paper. A simulation study is also presented which quantifies such a reduction.

A Checkpointing-Recovery Scheme for Domino-Free Distributed Systems

In this article we present Anonymous Readers Counting (ARC), a multi-word atomic (1,N) register algorithm for multi-core machines. ARC exploits Read-Modify-Write (RMW) instructions to coordinate the writer and reader threads in a wait-free manner and enables large-scale data sharing by admitting up to $(2^{32}-2)$ concurrent readers on off-the-shelf 64-bit machines, as opposed to the most advanced RMW-based approach which is limited to 58 readers on the same kind of machines. Further, ARC avoids multiple copies of the register content when accessing it—this is a problem that affects classical register algorithms based on atomic read/write operations on single words. Thus it allows for higher scalability with respect to the register size. Moreover, ARC explicitly reduces the overall power consumption, via a proper limitation of RMW instructions in case of read operations re-accessing a still-valid snapshot of the register content, and by showing constant time for read operations and amortized constant time for write operations. Our proposal has therefore a strong focus on real-world off-the-shelf architectures, allowing us to capture properties which benefit both performance and power consumption. A proof of correctness of our register algorithm is also provided, together with experimental data for a comparison with literature proposals. Beyond assessing ARC on physical platforms, we carry out as well an experimentation on virtualized infrastructures, which shows the resilience of wait-free synchronization as provided by ARC with respect to CPU-steal times, proper of modern paradigms such as cloud computing. Finally, we discuss how to extend ARC for scenarios with multiple writers and multiple readers—the so called (M,N) register. This is achieved not by changing the operations (and their wait-free nature) executed along the critical path of the threads, rather only changing the ratio between the number of buffers keeping the register snapshots and the number of threads to coordinate, as well as the number of bits used for counting readers within a 64-bit mask accessed via RMW instructions—just depending on the target balance between the number of readers and the number of writers to be supported.

/pdf/anonymous-readers-counting-a-wait-free-multi-word-atomic-4sbgf61slq.pdf

Anonymous Readers Counting: A Wait-Free Multi-Word Atomic Register Algorithm for Scalable Data Sharing on Multi-Core Machines

This paper presents RAMSES, a framework for easily specifying agent-based discrete event models entailing both environment and agent entities RAMSES offers parallel execution capabilities based on speculative event processing and an innovative software reversibility technique that copes with state restore in case the run slides along a non-consistent speculative path Reversibility in RAMSES relies on transparent static software instrumentation, thus allowing the model developer to concentrate on the actual forward-execution logic of the simulation events occurring in the system An experimental assessment of RAMSES is also presented, which is aimed at determining its run-time effectiveness and its potential for simplifying the development of agent-based models when compared to other (general purpose) speculative frameworks for parallel discrete event simulation

/pdf/ramses-reversibility-based-agent-modeling-and-simulation-4y8l1sfl02.pdf

Francesco Quaglia

Papers

Metronome: adaptive and precise intermittent packet retrieval in DPDK

Performance analysis of adaptive wormhole routing in a two-dimensional torus

A Checkpointing-Recovery Scheme for Domino-Free Distributed Systems

Anonymous Readers Counting: A Wait-Free Multi-Word Atomic Register Algorithm for Scalable Data Sharing on Multi-Core Machines

RAMSES: Reversibility-Based Agent Modeling and Simulation Environment with Speculation-Support