Learned Garbage Collection

Several programming languages use garbage collectors (GCs) to automatically manage memory for the programmer. Such collectors must decide when to look for unreachable objects to free, which can have a large performance impact on some applications. In this preliminary work, we propose a design for a learned garbage collector that autonomously learns over time when to perform collections. By using reinforcement learning, our design can incorporate user-defined reward functions, allowing an autonomous garbage collector to learn to optimize the exact metric the user desires (e.g., request latency or queries per second). We conduct an initial experimental study on a prototype, demonstrating that an approach based on tabular Q learning may be promising

Learned Query Superoptimization

Traditional query optimizers are designed to be fast and stateless: each query is quickly optimized using approximate statistics, sent off to the execution engine, and promptly forgotten. Recent work on learned query optimization have shown that it is possible for a query optimizer to "learn from its mistakes," correcting erroneous query plans the next time a plan is produced. But what if query optimizers could avoid mistakes entirely? This paper presents the idea of learned query superoptimization. A new generation of query superoptimizers could autonomously experiment to discover optimal plans using exploration-driven algorithms, iterative Bayesian optimization, and program synthesis. While such superoptimizers will take significantly longer to optimize a given query, superoptimizers have the potential to massively accelerate a large number of important repetitive queries being executed on data systems today

ControlFlag: A Self-supervised Idiosyncratic Pattern Detection System for Software Control Structures

Software debugging has been shown to utilize upwards of 50% of developers’ time. Machine programming, the field concerned with the automation of software (and hardware) development, has recently made progress in both research and production-quality automated debugging systems. In this paper, we present ControlFlag, a system that detects possible idiosyncratic violations in software control structures. ControlFlag also suggests possible corrections in the event a true error is detected. A novelty of ControlFlag is that it is entirely self-supervised; that is, it requires no labels to learn about the potential idiosyncratic programming pattern violations. In addition to presenting ControlFlag’s design, we also provide an abbreviated experimental evaluation

AdaChain: A Learned Adaptive Blockchain

This paper presents AdaChain, a learning-based blockchain framework that adaptively chooses the best permissioned blockchain architecture in order to optimize effective throughput for dynamic transaction workloads. AdaChain addresses the challenge in the Blockchain-as-a-Service (BaaS) environments, where a large variety of possible smart contracts are deployed with different workload characteristics. AdaChain supports automatically adapting to an underlying, dynamically changing workload through the use of reinforcement learning. When a promising architecture is identified, AdaChain switches from the current architecture to the promising one at runtime in a way that respects correctness and security concerns. Experimentally, we show that AdaChain can converge quickly to optimal architectures under changing workloads, significantly outperform fixed architectures in terms of the number of successfully committed transactions, all while incurring low additional overhead

Adding Machine Intelligence to Hybrid Memory Management

Computing platforms increasingly incorporate heterogeneous memory hardware technologies, as a way to scale application performance, memory capacities and achieve cost effectiveness. However, this heterogeneity, along with the greater irregularity in the behavior of emerging workloads, render existing hybrid memory management approaches ineffective, calling for more intelligent methods. To this end, this thesis reveals new insights, develops novel methods and contributes system-level mechanisms towards the practical integration of machine learning to hybrid memory management, boosting application performance and system resource efficiency. First, this thesis builds Kleio; a hybrid memory page scheduler with machine intelligence. Kleio deploys Recurrent Neural Networks to learn memory access patterns at a page granularity and to improve upon the selection of dynamic page migrations across the memory hardware components. Kleio cleverly focuses the machine learning on the page subset whose timely movement will reveal most application performance improvement, while preserving history-based lightweight management for the rest of the pages. In this way, Kleio bridges on average 80% of the relative existing performance gap, while laying the grounds for practical machine intelligent data management with manageable learning overheads. In addition, this thesis contributes three system-level mechanisms to further boost application performance and reduce the operational and learning overheads of machine learning-based hybrid memory management. First, this thesis builds Cori; a system-level solution for tuning the operational frequency of periodic page schedulers for hybrid memories. Cori leverages insights on data reuse times to fine tune the page migration frequency in a lightweight manner. Second, this thesis contributes Coeus; a page grouping mechanism for page schedulers like Kleio. Coeus leverages Cori’s data reuse insights to tune the granularity at which patterns are interpreted by the page scheduler and enable the training of a single Recurrent Neural Network per page cluster, reducing by 3x the model training times. The combined effects of Cori and Coeus provide 3x additional performance improvements to Kleio. Finally, this thesis proposes Cronus; an image-based page selector for page schedulers like Kleio. Cronus uses visualization to accelerate the process of selecting which page patterns should be managed with machine learning, reducing by 75x the operational overheads of Kleio. Cronus lays the foundations for future use of visualization and computer vision methods in memory management, such as image-based memory access pattern classification, recognition and prediction.Ph.D