Adaptiveness, Asynchrony, and Resource Efficiency in Parallel Stochastic Gradient Descent

Accelerated digitalization and sensor deployment in society in recent years poses critical challenges for associated data processing and analysis infrastructure to scale, and the field of big data, targeting methods for storing, processing, and revealing patterns in huge data sets, has surged. Artificial Intelligence (AI) models are used diligently in standard Big Data pipelines due to their tremendous success across various data analysis tasks, however exponential growth in Volume, Variety and Velocity of Big Data (known as its three V’s) in recent years require associated complexity in the AI models that analyze it, as well as the Machine Learning (ML) processes required to train them. In order to cope, parallelism in ML is standard nowadays, with the aim to better utilize contemporary computing infrastructure, whether it being shared-memory multi-core CPUs, or vast connected networks of IoT devices engaging in Federated Learning (FL).Stochastic Gradient Descent (SGD) serves as the backbone of many of the most popular ML methods, including in particular Deep Learning. However, SGD has inherently sequential semantics, and is not trivially parallelizable without imposing strict synchronization, with associated bottlenecks. Asynchronous SGD (AsyncSGD), which relaxes the original semantics, has gained significant interest in recent years due to promising results that show speedup in certain contexts. However, the relaxed semantics that asynchrony entails give rise to fundamental questions regarding AsyncSGD, relating particularly to its stability and convergence rate in practical applications.This thesis explores vital knowledge gaps of AsyncSGD, and contributes in particular to: Theoretical frameworks – Formalization of several key notions related to the impact of asynchrony on the convergence, guiding future development of AsyncSGD implementations; Analytical results – Asymptotic convergence bounds under realistic assumptions. Moreover, several technical solutions are proposed, targeting in particular: Stability – Reducing the number of non-converging executions and the associated wasted energy; Speedup – Improving convergence time and reliability with instance-based adaptiveness; Elasticity – Resource-efficiency by avoiding over-parallelism, and thereby improving stability and saving computing resources. The proposed methods are evaluated on several standard DL benchmarking applications and compared to relevant baselines from previous literature. Key results include: (i) persistent speedup compared to baselines, (ii) increased stability and reduced risk for non-converging executions, (iii) reduction in the overall memory footprint (up to 17%), as well as the consumed computing resources (up to 67%).In addition, along with this thesis, an open-source implementation is published, that connects high-level ML operations with asynchronous implementations with fine-grained memory operations, leveraging future research for efficient adaptation of AsyncSGD for practical applications

Adaptiveness and Lock-free Synchronization in Parallel Stochastic Gradient Descent

The emergence of big data in recent years due to the vast societal digitalization and large-scale sensor deployment has entailed significant interest in machine learning methods to enable automatic data analytics. In a majority of the learning algorithms used in industrial as well as academic settings, the first-order iterative optimization procedure Stochastic gradient descent (SGD), is the backbone. However, SGD is often time-consuming, as it typically requires several passes through the entire dataset in order to converge to a solution of sufficient quality.In order to cope with increasing data volumes, and to facilitate accelerated processing utilizing contemporary hardware, various parallel SGD variants have been proposed. In addition to traditional synchronous parallelization schemes, asynchronous ones have received particular interest in recent literature due to their improved ability to scale due to less coordination, and subsequently waiting time. However, asynchrony implies inherent challenges in understanding the execution of the algorithm and its convergence properties, due the presence of both stale and inconsistent views of the shared state.In this work, we aim to increase the understanding of the convergence properties of SGD for practical applications under asynchronous parallelism and develop tools and frameworks that facilitate improved convergence properties as well as further research and development. First, we focus on understanding the impact of staleness, and introduce models for capturing the dynamics of parallel execution of SGD. This enables (i) quantifying the statistical penalty on the convergence due to staleness and (ii) deriving an adaptation scheme, introducing a staleness-adaptive SGD variant MindTheStep-AsyncSGD, which provably reduces this penalty. Second, we aim at exploring the impact of synchronization mechanisms, in particular consistency-preserving ones, and the overall effect on the convergence properties. To this end, we propose LeashedSGD, an extensible algorithmic framework supporting various synchronization mechanisms for different degrees of consistency, enabling in particular a lock-free and consistency-preserving implementation. In addition, the algorithmic construction of Leashed-SGD enables dynamic memory allocation, claiming memory only when necessary, which reduces the overall memory footprint. We perform an extensive empirical study, benchmarking the proposed methods, together with established baselines, focusing on the prominent application of Deep Learning for image classification on the benchmark datasets MNIST and CIFAR, showing significant improvements in converge time for Leashed-SGD and MindTheStep-AsyncSGD

Estimating data divergence in cloud computing storage systems

Dissertação para obtenção do Grau de Mestre em Engenharia InformáticaMany internet services are provided through cloud computing infrastructures that are composed of multiple data centers. To provide high availability and low latency, data is replicated in machines in different data centers, which introduces the complexity of guaranteeing that clients view data consistently. Data stores often opt for a relaxed approach to replication, guaranteeing only eventual consistency, since it improves latency of operations. However, this may lead to replicas having different values for the same data. One solution to control the divergence of data in eventually consistent systems is the usage of metrics that measure how stale data is for a replica. In the past, several algorithms have been proposed to estimate the value of these metrics in a deterministic way. An alternative solution is to rely on probabilistic metrics that estimate divergence with a certain degree of certainty. This relaxes the need to contact all replicas while still providing a relatively accurate measurement. In this work we designed and implemented a solution to estimate the divergence of data in eventually consistent data stores, that scale to many replicas by allowing clientside caching. Measuring the divergence when there is a large number of clients calls for the development of new algorithms that provide probabilistic guarantees. Additionally, unlike previous works, we intend to focus on measuring the divergence relative to a state that can lead to the violation of application invariants.Partially funded by project PTDC/EIA EIA/108963/2008 and by an ERC Starting Grant, Agreement Number 30773

Optimistic replication

Data replication is a key technology in distributed data sharing systems, enabling higher availability and performance. This paper surveys optimistic replication algorithms that allow replica contents to diverge in the short term, in order to support concurrent work practices and to tolerate failures in low-quality communication links. The importance of such techniques is increasing as collaboration through wide-area and mobile networks becomes popular. Optimistic replication techniques are different from traditional “pessimistic ” ones. Instead of synchronous replica coordination, an optimistic algorithm propagates changes in the background, discovers conflicts after they happen and reaches agreement on the final contents incrementally. We explore the solution space for optimistic replication algorithms. This paper identifies key challenges facing optimistic replication systems — ordering operations, detecting and resolving conflicts, propagating changes efficiently, and bounding replica divergence — and provides a comprehensive survey of techniques developed for addressing these challenges

Patterns of Scalable Bayesian Inference

Datasets are growing not just in size but in complexity, creating a demand for rich models and quantification of uncertainty. Bayesian methods are an excellent fit for this demand, but scaling Bayesian inference is a challenge. In response to this challenge, there has been considerable recent work based on varying assumptions about model structure, underlying computational resources, and the importance of asymptotic correctness. As a result, there is a zoo of ideas with few clear overarching principles. In this paper, we seek to identify unifying principles, patterns, and intuitions for scaling Bayesian inference. We review existing work on utilizing modern computing resources with both MCMC and variational approximation techniques. From this taxonomy of ideas, we characterize the general principles that have proven successful for designing scalable inference procedures and comment on the path forward

Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis

Deep Neural Networks (DNNs) are becoming an important tool in modern computing applications. Accelerating their training is a major challenge and techniques range from distributed algorithms to low-level circuit design. In this survey, we describe the problem from a theoretical perspective, followed by approaches for its parallelization. We present trends in DNN architectures and the resulting implications on parallelization strategies. We then review and model the different types of concurrency in DNNs: from the single operator, through parallelism in network inference and training, to distributed deep learning. We discuss asynchronous stochastic optimization, distributed system architectures, communication schemes, and neural architecture search. Based on those approaches, we extrapolate potential directions for parallelism in deep learning

A review on DISC 2005, the 19th International Symposium on Distributed Computing

DISC is an international symposium on the theory, design, analysis, implementation and application of distributed systems and networks. The well-known International Symposium on Distributed Computing is organized annually in cooperation with the European Association for Theoretical Computer Science (EATCS). This is a review on the 19th International Symposium on Distributed Computing, which took place in Kraków, Poland, on September 26--29, 2005. The proceedings of DISC 2005 are published by Springer, as volume 3724 of the Lecture Notes in Computer Science (LNCS) series. The conference website can be found at www.mimuw.edu.pl/~disc2005.Postprint (published version

Benchmarking Eventually Consistent Distributed Storage Systems

Cloud storage services and NoSQL systems typically offer only "Eventual Consistency", a rather weak guarantee covering a broad range of potential data consistency behavior. The degree of actual (in-)consistency, however, is unknown. This work presents novel solutions for determining the degree of (in-)consistency via simulation and benchmarking, as well as the necessary means to resolve inconsistencies leveraging this information

Building Scalable and Consistent Distributed Databases Under Conflicts

Distributed databases, which rely on redundant and distributed storage across multiple servers, are able to provide mission-critical data management services at large scale. Parallelism is the key to the scalability of distributed databases, but concurrent queries having conflicts may block or abort each other when strong consistency is enforced using rigorous concurrency control protocols. This thesis studies the techniques of building scalable distributed databases under strong consistency guarantees even in the face of high contention workloads. The techniques proposed in this thesis share a common idea, conflict mitigation, meaning mitigating conflicts by rescheduling operations in the concurrency control in the first place instead of resolving contending conflicts. Using this idea, concurrent queries under conflicts can be executed with high parallelism. This thesis explores this idea on both databases that support serializable ACID (atomic, consistency, isolation, durability) transactions, and eventually consistent NoSQL systems. First, the epoch-based concurrency control (ECC) technique is proposed in ALOHA-KV, a new distributed key-value store that supports high performance read-only and write-only distributed transactions. ECC demonstrates that concurrent serializable distributed transactions can be processed in parallel with low overhead even under high contention. With ECC, a new atomic commitment protocol is developed that only requires amortized one round trip for a distributed write-only transaction to commit in the absence of failures. Second, a novel paradigm of serializable distributed transaction processing is developed to extend ECC with read-write transaction processing support. This paradigm uses a newly proposed database operator, functors, which is a placeholder for the value of a key, which can be computed asynchronously in parallel with other functor computations of the same or other transactions. Functor-enabled ECC achieves more fine-grained concurrency control than transaction level concurrency control, and it never aborts transactions due to read-write or write-write conflicts but allows transactions to fail due to logic errors or constraint violations while guaranteeing serializability. Lastly, this thesis explores consistency in the eventually consistent system, Apache Cassandra, for an investigation of the consistency violation, referred to as "consistency spikes". This investigation shows that the consistency spikes exhibited by Cassandra are strongly correlated with garbage collection, particularly the "stop-the-world" phase in the Java virtual machine. Thus, delaying read operations arti cially at servers immediately after a garbage collection pause can virtually eliminate these spikes. All together, these techniques allow distributed databases to provide scalable and consistent storage service