QuakeTM: Parallelizing a complex serial application using transactional memory

'Is transactional memory useful?' is the question that cannot be answered until we provide substantial applications that can evaluate its capabilities. While existing TM applications can partially answer the above question, and are useful in the sense that they provide a first-order TM experimentation framework, they serve only as a proof of concept and fail to make a conclusive case for wide adoption by the general computing community. This work presents QuakeTM, a multiplayer game server; a complex real life TM application that was parallelized from the serial version with TM-specific considerations in mind. QuakeTM consists of 27,600 lines of code spread among 49 files and exhibits irregular parallelism and coarse-grain transactions with large read and write sets. In spite of its complexity, we show that QuakeTM does scale, however more effort is needed to decrease the overhead and the abort rate of current software transactional memory systems. We give insights into development challenges, suggest techniques to solve them and provide extensive analysis of transactional behavior of QuakeTM, with an emphasis and discussion of the TM promise of making parallel programming easy.Postprint (published version

Adaptive thread scheduling techniques for improving scalability of software transactional memory

Software transactional memory (STM) enhances both ease-of-use and concurrency, and is considered state-of-the-art for parallel applications to scale on modern multi-core hardware. However, there are certain situations where STM performs even worse than traditional locks. Upon hotspots where most threads contend over a few pieces of shared data, going transactional will result in excessive conflicts and aborts that adversely degrade performance. We present a new design of adaptive thread scheduler that manages concurrency when the system is about entering and leaving hotspots. The scheduler controls the number of threads spawning new transactions according to the live commit throughput. We implemented two feedback-control policies called Throttle and Probe to realize this adaptive scheduling. Performance evaluation with the STAMP benchmarks shows that enabling Throttle and Probe obtain best-case speedups of 87.5% and 108.7% respectively.postprintThe 10th IASTED International Conference on Parallel and Distributed Computing and Networks (PDCN 2011), Innsbruck, Austria, 15-17 February 2011. In Proceedings of the 10th IASTED-PDCN, 2011, p. 91-9

Enhancing the efficiency and practicality of software transactional memory on massively multithreaded systems

Chip Multithreading (CMT) processors promise to deliver higher performance by running more than one stream of instructions in parallel. To exploit CMT's capabilities, programmers have to parallelize their applications, which is not a trivial task. Transactional Memory (TM) is one of parallel programming models that aims at simplifying synchronization by raising the level of abstraction between semantic atomicity and the means by which that atomicity is achieved. TM is a promising programming model but there are still important challenges that must be addressed to make it more practical and efficient in mainstream parallel programming. The first challenge addressed in this dissertation is that of making the evaluation of TM proposals more solid with realistic TM benchmarks and being able to run the same benchmarks on different STM systems. We first introduce a benchmark suite, RMS-TM, a comprehensive benchmark suite to evaluate HTMs and STMs. RMS-TM consists of seven applications from the Recognition, Mining and Synthesis (RMS) domain that are representative of future workloads. RMS-TM features current TM research issues such as nesting and I/O inside transactions, while also providing various TM characteristics. Most STM systems are implemented as user-level libraries: the programmer is expected to manually instrument not only transaction boundaries, but also individual loads and stores within transactions. This library-based approach is increasingly tedious and error prone and also makes it difficult to make reliable performance comparisons. To enable an "apples-to-apples" performance comparison, we then develop a software layer that allows researchers to test the same applications with interchangeable STM back ends. The second challenge addressed is that of enhancing performance and scalability of TM applications running on aggressive multi-core/multi-threaded processors. Performance and scalability of current TM designs, in particular STM desings, do not always meet the programmer's expectation, especially at scale. To overcome this limitation, we propose a new STM design, STM2, based on an assisted execution model in which time-consuming TM operations are offloaded to auxiliary threads while application threads optimistically perform computation. Surprisingly, our results show that STM2 provides, on average, speedups between 1.8x and 5.2x over state-of-the-art STM systems. On the other hand, we notice that assisted-execution systems may show low processor utilization. To alleviate this problem and to increase the efficiency of STM2, we enriched STM2 with a runtime mechanism that automatically and adaptively detects application and auxiliary threads' computing demands and dynamically partition hardware resources between the pair through the hardware thread prioritization mechanism implemented in POWER machines. The third challenge is to define a notion of what it means for a TM program to be correctly synchronized. The current definition of transactional data race requires all transactions to be totally ordered "as if'' serialized by a global lock, which limits the scalability of TM designs. To remove this constraint, we first propose to relax the current definition of transactional data race to allow a higher level of concurrency. Based on this definition we propose the first practical race detection algorithm for C/C++ applications (TRADE) and implement the corresponding race detection tool. Then, we introduce a new definition of transactional data race that is more intuitive, transparent to the underlying TM implementation, can be used for a broad set of C/C++ TM programs. Based on this new definition, we proposed T-Rex, an efficient and scalable race detection tool for C/C++ TM applications. Using TRADE and T-Rex, we have discovered subtle transactional data races in widely-used STAMP applications which have not been reported in the past

Static detection of anomalies in transactional memory programs

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do Grau de Mestre em Engenharia InformáticaTransactional Memory (TM) is an approach to concurrent programming based on the transactional semantics borrowed from database systems. In this paradigm, a transaction is a sequence of actions that may execute in a single logical instant, as though it was the only one being executed at that moment. Unlike concurrent systems based in locks, TM does not enforce that a single thread is performing the guarded operations. Instead, like in database systems, transactions execute concurrently, and the effects of a transaction are undone in case of a conflict, as though it never happened. The advantages of TM are an easier and less error-prone programming model, and a potential increase in scalability and performance. In spite of these advantages, TM is still a young and immature technology, and has still to become an established programming model. It still lacks the paraphernalia of tools and standards which we have come to expect from a widely used programming paradigm. Testing and analysis techniques and algorithms for TM programs are also just starting to be addressed by the scientific community, making this a leading research work is many of these aspects. This work is aimed at statically identifying possible runtime anomalies in TMprograms. We addressed both low-level dataraces in TM programs, as well as high-level anomalies resulting from incorrect splitting of transactions. We have defined and implemented an approach to detect low-level dataraces in TM programs by converting all the memory transactions into monitor protected critical regions, synchronized on a newly generated global lock. To validate the approach, we have applied our tool to a set of tests, adapted from the literature, that contain well documented errors. We have also defined and implemented a new approach to static detection of high-level concurrency anomalies in TM programs. This new approach works by conservatively tracing transactions, and matching the interference between each consecutive pair of transactions against a set of defined anomaly patterns. Once again, the approach was validated with well documented tests adapted from the literature

Tailoring Transactional Memory to Real-World Applications

Transactional Memory (TM) promises to provide a scalable mechanism for synchronizationin concurrent programs, and to offer ease-of-use benefits to programmers. Since multiprocessorarchitectures have dominated CPU design, exploiting parallelism in program

Dagstuhl News January - December 2008

"Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic

The psychological adaptation of psychologists in clinical training : the role of cognition, coping and social support

Objectives: The current study sought to profile the psychological adaptation of psychologists in clinical training and examine the extent to which appraisal, coping and social support mediate and/or moderate psychological adaptation. Design: A mixed within-persons and between-persons design was used. Methods: A sample of 183 psychologists in clinical training (60.2 per cent response rate) from 15 British clinical psychology training courses participated at time one, 167 of whom participated at time two one year later (91.3 per cent of the time one sample). They completed measures of cognition, coping and social support. A multidimensional assessment of psychological adaptation included measures of perceived stress, anxiety and depression. Results: Trainee clinical psychologists reported high levels of stress, but as a group did not experience extensive problems of psychological adaptation in terms of anxiety, depression, selfesteem problems, marital problems, family problems, external stressors, interpersonal conflict, work adjustment or substance abuse. However, a significant subgroup reported self-esteem problems, work adjustment problems, depression and anxiety. Gender, age, year of training and training course were related to psychological adaptation. Appraisal processes, coping and social support predicted a significant amount of variation in psychological adaptation. Appraisals of threat, avoidance coping, emotional support from clinical supervisors, emotional support from courses and emotional support from a confidante at home all predicted the variance in psychological adaptation over time. Conclusions and implications: The findings were discussed in terms of a cognitive model of stress and adaptation. Implications for trainee clinical psychologists, training courses and the clinical psychology profession were considered

The Effects of Perceptual Fluency on Emotional Word Recognition

To investigate if making a word harder to read attenuates emotional influences like valence and arousal, we used a sample of Warriner and colleagues’ (2013) corpus with valence and arousal norms, a font manipulation from the perceptual fluency paradigm, and a word naming task. We found that, contrary to our hypotheses, emotional influences of words on RT were not attenuated in the disfluent condition; in fact, disfluency seemed to amplify the facilitative effects of high arousal. These results suggest that models of word recognition should consider the role that emotions play in recognition. They also provide limited support to models that emphasize the importance of perceptual features (e.g., Fritsch & Kuchinke, 2013) as well as the facilitative effect of high valence words (e.g., automatic vigilance), but, ultimately, do not fit into one specific theoretical framework. This study also represents the first application of perceptual fluency in emotional word recognition

Integrative bioinformatics applications for complex human disease contexts

This thesis presents new methods for the analysis of high-throughput data from modern sources in the context of complex human diseases, at the example of a bioinformatics analysis workflow. New measurement techniques improve the resolution with which cellular and molecular processes can be monitored. While RNA sequencing (RNA-seq) measures mRNA expression, single-cell RNA-seq (scRNA-seq) resolves this on a per-cell basis. Long-read sequencing is increasingly used in genomics. With imaging mass spectrometry (IMS) the protein level in tissues is measured spatially resolved. All these techniques induce specific challenges, which need to be addressed with new computational methods. Collecting knowledge with contextual annotations is important for integrative data analyses. Such knowledge is available through large literature repositories, from which information, such as miRNA-gene interactions, can be extracted using text mining methods. After aggregating this information in new databases, specific questions can be answered with traceable evidence. The combination of experimental data with these databases offers new possibilities for data integrative methods and for answering questions relevant for complex human diseases. Several data sources are made available, such as literature for text mining miRNA-gene interactions (Chapter 2), next- and third-generation sequencing data for genomics and transcriptomics (Chapters 4.1, 5), and IMS for spatially resolved proteomics (Chapter 4.4). For these data sources new methods for information extraction and pre-processing are developed. For instance, third-generation sequencing runs can be monitored and evaluated using the poreSTAT and sequ-into methods. The integrative (down-stream) analyses make use of these (heterogeneous) data sources. The cPred method (Chapter 4.2) for cell type prediction from scRNA-seq data was successfully applied in the context of the SARS-CoV-2 pandemic. The robust differential expression (DE) analysis pipeline RoDE (Chapter 6.1) contains a large set of methods for (differential) data analysis, reporting and visualization of RNA-seq data. Topics of accessibility of bioinformatics software are discussed along practical applications (Chapter 3). The developed miRNA-gene interaction database gives valuable insights into atherosclerosis-relevant processes and serves as regulatory network for the prediction of active miRNA regulators in RoDE (Chapter 6.1). The cPred predictions, RoDE results, scRNA-seq and IMS data are unified as input for the 3D-index Aorta3D (Chapter 6.2), which makes atherosclerosis related datasets browsable. Finally, the scRNA-seq analysis with subsequent cPred cell type prediction, and the robust analysis of bulk-RNA-seq datasets, led to novel insights into COVID-19. Taken all discussed methods together, the integrative analysis methods for complex human disease contexts have been improved at essential positions.Die Dissertation beschreibt Methoden zur Prozessierung von aktuellen Hochdurchsatzdaten, sowie Verfahren zu deren weiterer integrativen Analyse. Diese findet Anwendung vor allem im Kontext von komplexen menschlichen Krankheiten. Neue Messtechniken erlauben eine detailliertere Beobachtung biomedizinischer Prozesse. Mit RNA-Sequenzierung (RNA-seq) wird mRNA-Expression gemessen, mit Hilfe von moderner single-cell-RNA-seq (scRNA-seq) sogar für (sehr viele) einzelne Zellen. Long-Read-Sequenzierung wird zunehmend zur Sequenzierung ganzer Genome eingesetzt. Mittels bildgebender Massenspektrometrie (IMS) können Proteine in Geweben räumlich aufgelöst quantifiziert werden. Diese Techniken bringen spezifische Herausforderungen mit sich, die mit neuen bioinformatischen Methoden angegangen werden müssen. Für die integrative Datenanalyse ist auch die Gewinnung von geeignetem Kontextwissen wichtig. Wissenschaftliche Erkenntnisse werden in Artikeln veröffentlicht, die über große Literaturdatenbanken zugänglich sind. Mittels Textmining können daraus Informationen extrahiert werden, z.B. miRNA-Gen-Interaktionen, die in eigenen Datenbank aggregiert werden um spezifische Fragen mit nachvollziehbaren Belegen zu beantworten. In Kombination mit experimentellen Daten bieten sich so neue Möglichkeiten für integrative Methoden. Durch die Extraktion von Rohdaten und deren Vorprozessierung werden mehrere Datenquellen erschlossen, wie z.B. Literatur für Textmining von miRNA-Gen-Interaktionen (Kapitel 2), Long-Read- und RNA-seq-Daten für Genomics und Transcriptomics (Kapitel 4.2, 5) und IMS für Protein-Messungen (Kapitel 4.4). So dienen z.B. die poreSTAT und sequ-into Methoden der Vorprozessierung und Auswertung von Long-Read-Sequenzierungen. In der integrativen (down-stream) Analyse werden diese (heterogenen) Datenquellen verwendet. Für die Bestimmung von Zelltypen in scRNA-seq-Experimenten wurde die cPred-Methode (Kapitel 4.2) erfolgreich im Kontext der SARS-CoV-2-Pandemie eingesetzt. Auch die robuste Pipeline RoDE fand dort Anwendung, die viele Methoden zur (differentiellen) Datenanalyse, zum Reporting und zur Visualisierung bereitstellt (Kapitel 6.1). Themen der Benutzbarkeit von (bioinformatischer) Software werden an Hand von praktischen Anwendungen diskutiert (Kapitel 3). Die entwickelte miRNA-Gen-Interaktionsdatenbank gibt wertvolle Einblicke in Atherosklerose-relevante Prozesse und dient als regulatorisches Netzwerk für die Vorhersage von aktiven miRNA-Regulatoren in RoDE (Kapitel 6.1). Die cPred-Methode, RoDE-Ergebnisse, scRNA-seq- und IMS-Daten werden im 3D-Index Aorta3D (Kapitel 6.2) zusammengeführt, der relevante Datensätze durchsuchbar macht. Die diskutierten Methoden führen zu erheblichen Verbesserungen für die integrative Datenanalyse in komplexen menschlichen Krankheitskontexten

Architectural Principles for Database Systems on Storage-Class Memory

Database systems have long been optimized to hide the higher latency of storage media, yielding complex persistence mechanisms. With the advent of large DRAM capacities, it became possible to keep a full copy of the data in DRAM. Systems that leverage this possibility, such as main-memory databases, keep two copies of the data in two different formats: one in main memory and the other one in storage. The two copies are kept synchronized using snapshotting and logging. This main-memory-centric architecture yields nearly two orders of magnitude faster analytical processing than traditional, disk-centric ones. The rise of Big Data emphasized the importance of such systems with an ever-increasing need for more main memory. However, DRAM is hitting its scalability limits: It is intrinsically hard to further increase its density. Storage-Class Memory (SCM) is a group of novel memory technologies that promise to alleviate DRAM’s scalability limits. They combine the non-volatility, density, and economic characteristics of storage media with the byte-addressability and a latency close to that of DRAM. Therefore, SCM can serve as persistent main memory, thereby bridging the gap between main memory and storage. In this dissertation, we explore the impact of SCM as persistent main memory on database systems. Assuming a hybrid SCM-DRAM hardware architecture, we propose a novel software architecture for database systems that places primary data in SCM and directly operates on it, eliminating the need for explicit IO. This architecture yields many benefits: First, it obviates the need to reload data from storage to main memory during recovery, as data is discovered and accessed directly in SCM. Second, it allows replacing the traditional logging infrastructure by fine-grained, cheap micro-logging at data-structure level. Third, secondary data can be stored in DRAM and reconstructed during recovery. Fourth, system runtime information can be stored in SCM to improve recovery time. Finally, the system may retain and continue in-flight transactions in case of system failures. However, SCM is no panacea as it raises unprecedented programming challenges. Given its byte-addressability and low latency, processors can access, read, modify, and persist data in SCM using load/store instructions at a CPU cache line granularity. The path from CPU registers to SCM is long and mostly volatile, including store buffers and CPU caches, leaving the programmer with little control over when data is persisted. Therefore, there is a need to enforce the order and durability of SCM writes using persistence primitives, such as cache line flushing instructions. This in turn creates new failure scenarios, such as missing or misplaced persistence primitives. We devise several building blocks to overcome these challenges. First, we identify the programming challenges of SCM and present a sound programming model that solves them. Then, we tackle memory management, as the first required building block to build a database system, by designing a highly scalable SCM allocator, named PAllocator, that fulfills the versatile needs of database systems. Thereafter, we propose the FPTree, a highly scalable hybrid SCM-DRAM persistent B+-Tree that bridges the gap between the performance of transient and persistent B+-Trees. Using these building blocks, we realize our envisioned database architecture in SOFORT, a hybrid SCM-DRAM columnar transactional engine. We propose an SCM-optimized MVCC scheme that eliminates write-ahead logging from the critical path of transactions. Since SCM -resident data is near-instantly available upon recovery, the new recovery bottleneck is rebuilding DRAM-based data. To alleviate this bottleneck, we propose a novel recovery technique that achieves nearly instant responsiveness of the database by accepting queries right after recovering SCM -based data, while rebuilding DRAM -based data in the background. Additionally, SCM brings new failure scenarios that existing testing tools cannot detect. Hence, we propose an online testing framework that is able to automatically simulate power failures and detect missing or misplaced persistence primitives. Finally, our proposed building blocks can serve to build more complex systems, paving the way for future database systems on SCM