Publications de Anastasia Ailamaki

Publications associées (177)

Chaosity: Understanding Contemporary NUMA-architectures

Andreea Nica, Anastasia Ailamaki, Viktor Sanca, Syed Mohammad Aunn Raza, Hamish Mcniece Hill Nicholson

Modern hardware is increasingly complex, requiring increasing effort to understand in order to carefully engineer systems for optimal performance and effective utilization. Moreover, established design principles and assumptions are not portable to modern ...

2023

Improving instruction cache performance in OLTP

Anastasia Ailamaki

Instruction-cache misses account for up to 40%; of execution time in online transaction processing (OLTP) database workloads. In contrast to data cache misses, instruction misses cannot be overlapped with out-of-order execution. Chip design limitations do ...

Association for Computing Machinery2006

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

Anastasia Ailamaki, Raja Appuswamy, Manolis Karpathiotakis, 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

Characterization of the Impact of Hardware Islands on OLTP

Anastasia Ailamaki, Pinar Tözün, Ippokratis Pandis, Danica Porobic, Miguel Sérgio De Oliveira Branco

Modern hardware is abundantly parallel and increasingly heterogeneous. The numerous processing cores have non-uniform access latencies to the main memory and processor caches, which causes variability in the communication costs. Unfortunately, database sys ...

Springer Verlag2016

ADDICT: Advanced Instruction Chasing for Transactions

Anastasia Ailamaki, Pinar Tözün

Recent studies highlight that traditional transaction processing systems utilize the micro-architectural features of modern processors very poorly. L1 instruction cache and long-latency data misses dominate execution time. As a result, more than half of th ...

2014

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 consid ...

ACM2022

Optimizing Context-Enhanced Relational Joins

Anastasia Ailamaki, Viktor Sanca

Collecting data, extracting value, and combining insights from relational and context-rich sources of many modalities in data processing pipelines presents a challenge for traditional relational DBMS. While relational operators enable declarative and optim ...

IEEE2024

Static Typing Meets Adaptive Optimization: A Unified Approach to Recursive Queries

Anastasia Ailamaki, Martin Odersky, Anna Patricia Herlihy

Writing correct and efficient recursive SQL queries is exceptionally challenging because recursive queries risk returning incorrect results, throwing runtime exceptions, or never terminating. Failures in recursive query execution fall into two categories: ...

ACM2025

Scalable and Dynamically Balanced Shared-Everything OLTP with Physiological Partitioning

Anastasia Ailamaki, Pinar Tözün, Ippokratis Pandis, Frederick Ryan Johnson

Scaling the performance of shared-everything transaction processing systems to highly-parallel multicore hardware remains a challenge for database system designers. Recent proposals alleviate locking and logging bottlenecks in the system, leaving page latc ...

2013

Reactive and Proactive Sharing Across Concurrent Analytical Queries

Anastasia Ailamaki, Manoussos Gavriil Athanassoulis, Matthaios Alexandros Olma, Iraklis Psaroudakis

Today an ever increasing amount of data is collected and analyzed by researchers, businesses, and scientists in data warehouses (DW). In addition to the data size, the number of users and applications querying data grows exponentially. The increasing concu ...

2014

Micro-architectural Analysis of In-memory OLTP

Anastasia Ailamaki, Pinar Tözün, Danica Porobic, Utku Sirin

Micro-architectural behavior of traditional disk-based online transaction processing (OLTP) systems has been investigated extensively over the past couple of decades. Results show that traditional OLTP mostly under-utilize the available micro-architectural ...

2016

RUBIK: Efficient Threshold Queries on Massive Time Series

Anastasia Ailamaki, Thomas Heinis, Farhan Tauheed, Eleni Tzirita Zacharatou

An increasing number of applications from finance, meteorology, science and others are producing time series as output. The analysis of the vast amount of time series is key to understand the phenomena studied, particularly in the simulation sciences, wher ...

2015

ReCache: Reactive Caching for Fast Analytics over Heterogeneous Data

Anastasia Ailamaki, Manolis Karpathiotakis, Tahir Azim

As data continues to be generated at exponentially growing rates in heterogeneous formats, fast analytics to extract meaningful information is becoming increasingly important. Systems widely use in-memory caching as one of their primary techniques to speed ...

2017

Decoupling contention management from scheduling

Anastasia Ailamaki, Frederick Ryan Johnson, Radu Ioan Stoica

Many parallel applications exhibit unpredictable communication between threads, leading to contention for shared objects. The choice of contention management strategy impacts strongly the performance and scalability of these applications: spinning provides ...

2010

HPCache: Memory-Efficient OLAP Through Proportional Caching

Anastasia Ailamaki, Hamish Mcniece Hill Nicholson, Periklis Chrysogelos

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

PixelsDB: Serverless and NL-Aided Data Analytics with Flexible Service Levels and Prices

Anastasia Ailamaki

Serverless query processing has become increasingly popular due to its advantages, including automated resource management, high elasticity, and pay-as-you-go pricing. For users who are not system experts, serverless query processing greatly reduces the co ...

2024

LAQy: Efficient and Reusable Query Approximations via Lazy Sampling

Anastasia Ailamaki, Viktor Sanca, Periklis Chrysogelos

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

Online Updates on Data Warehouses via Judicious Use of Solid-State Storage

Anastasia Ailamaki, Radu Ioan Stoica, Manoussos Gavriil Athanassoulis

Data warehouses have been traditionally optimized for read-only query performance, allowing only offline updates at night, essentially trading off data freshness for performance. The need for 24x7 operations in global markets and the rise of online and oth ...

Assoc Computing Machinery2015

OLTP in Wonderland -- Where do cache misses come from in major OLTP components?

Anastasia Ailamaki, Pinar Tözün

For several decades, online transaction processing has been one of the main applications that drives innovations in the data management ecosystem, and in turn the database and computer architecture communities. Despite the novel approaches from industry an ...

ACM2013

PREDIcT: Towards Predicting the Runtime of Large Scale Iterative Analytics

Anastasia Ailamaki, Adrian Daniel Popescu

Machine learning algorithms are widely used today for analytical tasks such as data cleaning, data categorization, or data filtering. At the same time, the rise of social media motivates recent uptake in large scale graph processing. Both categories of alg ...

2013