Machine analytique

La machine analytique (analytical engine en anglais) est une machine à calculer programmable imaginée en 1834 par le mathématicien anglais Charles Babbage. Il ne la réalisera jamais (sauf pour un prototype inachevé), mais il passera le reste de sa vie à la concevoir dans les moindres détails. Le plus jeune de ses fils, Henry Babbage, en construira l'unité centrale (le moulin) et l'imprimante de 1880 à 1910. C'est pendant le développement d'une machine à calculer destinée au calcul et à l'impression de tables mathématiques (machine à différences) que Babbage eut l'idée d'y incorporer des cartes du métier Jacquard, dont la lecture séquentielle donnerait des instructions et des données à sa machine, et donc imagina, malgré des différences notables de fonctionnement (elle fonctionnait à la vapeur avec des roues et engrenages mécaniques), l'ancêtre des ordinateurs modernes. C'est au cours du développement de cette machine qu'Ada Lovelace formalise les idées de Babbage et développe le

Sampling-Based AQP in Modern Analytical Engines

Anastasia Ailamaki, Viktor Sanca

As the data volume grows, reducing the query execution times remains an elusive goal. While approximate query processing (AQP) techniques present a principled method to trade off accuracy for faster queries in analytics, the sample creation is often considered a second-class citizen. Modern analytical engines optimized for high bandwidth media and multi-core architectures only exacerbate existing inefficiencies, resulting in prohibitive query-time online sampling and longer preprocessing times in offline AQP systems. We demonstrate that the sampling operators can be practical in modern scale-up analytical systems. First, we evaluate three common sampling methods, identify algorithmic bottlenecks, and propose hardware-conscious optimizations. Second, we reduce the performance penalties of the added processing and sample materialization through system-aware operator design and compare the sample creation time to the matching relational operators of an in-memory JIT-compiled engine. The cost of data reduction with materialization is up to 2.5x of the equivalent group-by in the case of stratified sampling and virtually free (∼1x) for reasonable sample sizes of other strategies. As query processing starts to dominate the execution time, the gap between online and offline AQP methods diminishes.

ACM2022

Efficient Analytical Query Processing on CPU-GPU Hardware Platforms

Periklis Chrysogelos

Timely insights lead to business growth and scientific breakthroughs but require analytical engines that cope with the ever-increasing data processing needs. Analytical engines relied on rapid CPU improvements, yet the end of Dennard scaling stopped the free lunch and resulted in a heterogeneous hardware landscape that challenges existing analytical engines. First, each device has its own specialized execution model and architecture, impeding interoperability. Second, the diversity in device microarchitectures requires a diverse range of optimizations. Third, while the multitude of devices provides additional acceleration opportunities, moving the data across devices is costly. Finally, with networking bandwidths similar to intra-server device connections, the server boundaries are blurred, providing optimization opportunities and requiring careful orchestration to avoid wasting resources. In this thesis, we aim for engines tailored for heterogeneous hardware: abstracting out hardware heterogeneity to enable efficient execution across the devices despite their diversity. To this end, we design and implement techniques that are i) scalable through accelerator-level parallelism, and ii) efficient through query execution customization to the underlying accelerators and data transfer paths.Regarding scalability, we propose a unifying execution model and a throughput-oriented system view to enable on-the-fly multi-device orchestration without requiring knowledge about hardware specifics. In addition, by decoupling data and control flow, this thesis enables late and direct data transfers within and across servers.Regarding efficiency, we provide an execution model that limits operator instances to specific devices, enabling operators to customize themselves to a single device without concern for multi-device effects. In addition, by providing interconnect-aware transfer methods, this thesis minimizes the cost of offloading operations across devices.This thesis redesigns analytical engines to exploit hardware heterogeneity. Instead of trading hardware efficiency for accelerator-level scalability, this thesis embraces heterogeneity. Our design enables scalable analytics across CPU-GPU hardware and achieves the analytical performance of optimally combining a CPU- and a GPU-optimized engine. As a result, users benefit from faster insights without requiring large clusters of machines. The proposed accelerator-centric design paves the way toward analytical engines that benefit from hardware improvements across the hardware spectrum -- instead of relying on single-processor advancements.

EPFL2022

Efficient Analytical Query Processing on CPU-GPU Hardware Platforms

Periklis Chrysogelos

EPFL2022

Sampling-Based AQP in Modern Analytical Engines

Efficient Analytical Query Processing on CPU-GPU Hardware Platforms

Analytic twists of modular forms

Analytic twists of modular forms

Sampling-Based AQP in Modern Analytical Engines

Efficient Analytical Query Processing on CPU-GPU Hardware Platforms