Publications de Periklis Chrysogelos

Efficient and Reusable Lazy Sampling

Anastasia Ailamaki, Periklis Chrysogelos, Viktor Sanca

2024

HPCache: memory-efficient OLAP through proportional caching revisited

Anastasia Ailamaki, Periklis Chrysogelos, Hamish Mcniece Hill Nicholson

Analytical engines rely on in-memory data caching to avoid storage accesses and provide timely responses by keeping the most frequently accessed data in memory. Purely frequency- and time-based caching decisions, however, are a proxy of the expected query ...

Springer2023

One-shot Garbage Collection for In-memory OLTP through Temporality-aware Version Storage

Anastasia Ailamaki, Angelos Christos Anadiotis, Periklis Chrysogelos, Syed Mohammad Aunn Raza

Most modern in-memory online transaction processing (OLTP) engines rely on multi-version concurrency control (MVCC) to provide data consistency guarantees in the presence of conflicting data accesses. MVCC improves concurrency by generating a new version o ...

ACM2023

HetCache: Synergising NVMe Storage and GPU acceleration for Memory-Efficient Analytics

Anastasia Ailamaki, Periklis Chrysogelos, Syed Mohammad Aunn Raza, Hamish Mcniece Hill Nicholson

Accessing input data is a critical operation in data analytics: i) slow data access significantly degrades performance, and ii) storing everything in the fastest medium, i.e., memory, incurs high operational and hardware costs. Further, while GPUs offer in ...

2023

LAQy: Efficient and Reusable Query Approximations via Lazy Sampling

Anastasia Ailamaki, Periklis Chrysogelos, Viktor Sanca

Modern analytical engines rely on Approximate Query Processing (AQP) to provide faster response times than the hardware allows for exact query answering. However, existing AQP methods impose steep performance penalties as workload unpredictability increase ...

2023

HPCache: Memory-Efficient OLAP Through Proportional Caching

Anastasia Ailamaki, Periklis Chrysogelos, Hamish Mcniece Hill Nicholson

Analytical engines rely on in-memory caching to avoid disk accesses and provide timely responses by keeping the most frequently accessed data in memory. Purely frequency- & time-based caching decisions, however, are a proxy of the expected query execution ...

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 fr ...

EPFL2022

Boosting Efficiency of External Pipelines by Blurring Application Boundaries

Anastasia Ailamaki, Periklis Chrysogelos, Anna Patricia Herlihy

Modern application development addresses increasingly specialized problems using domain-specific utilities, such as Optical Code Recognition and standalone statistical tools. The diversity of tooling, combined with the ever-growing volume of data, requires ...

2022

Adaptive HTAP through Elastic Resource Scheduling

Anastasia Ailamaki, Angelos Christos Anadiotis, Periklis Chrysogelos, Syed Mohammad Aunn Raza

Modern Hybrid Transactional/Analytical Processing (HTAP) systems use an integrated data processing engine that performs analytics on fresh data, which are ingested from a transactional engine. HTAP systems typically consider data freshness at design time, ...

2020

Adaptive HTAP through Elastic Resource Scheduling

Anastasia Ailamaki, Angelos Christos Anadiotis, Periklis Chrysogelos, Syed Mohammad Aunn Raza

Modern Hybrid Transactional/Analytical Processing (HTAP) systems use an integrated data processing engine that performs analytics on fresh data, which are ingested from a transactional engine. HTAP systems typically consider data freshness at design time, ...

ASSOC COMPUTING MACHINERY2020

GPU-accelerated data management under the test of time

Anastasia Ailamaki, Panagiotis Sioulas, Angelos Christos Anadiotis, Periklis Chrysogelos, Syed Mohammad Aunn Raza

GPUs are becoming increasingly popular in large scale data center installations due to their strong, embarrassingly parallel, processing capabilities. Data management systems are riding the wave by using GPUs to accelerate query execution, mainly for analy ...

2020

Hardware-conscious Query Processing in GPU-accelerated Analytical Engines

Anastasia Ailamaki, Panagiotis Sioulas, Periklis Chrysogelos

In order to improve their power efficiency and computational capacity, modern servers are adopting hardware accelerators, especially GPUs. Modern analytical DMBS engines have been highly optimized for multi-core multi-CPU query execution, but lack the nece ...

2019

Hardware-conscious Hash-Joins on GPUs

Anastasia Ailamaki, Manolis Karpathiotakis, Raja Appuswamy, Panagiotis Sioulas, Periklis Chrysogelos

Traditionally, analytical database engines have used task parallelism provided by modern multisocket multicore CPUs for scaling query execution. Over the past few years, GPUs have started gaining traction as accelerators for processing analytical queries d ...

IEEE2019

HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines

Anastasia Ailamaki, Manolis Karpathiotakis, Raja Appuswamy, Periklis Chrysogelos

Modern server hardware is increasingly heterogeneous as hardware accelerators, such as GPUs, are used together with multicore CPUs to meet the computational demands of modern data analytics workloads. Unfortunately, query parallelization techniques used by ...

2019