Publications de Panagiotis Sioulas

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Analytical workloads are evolving as the number of users surges and applications that submit queries in batches become popular. However, traditional analytical databases that optimize-then-execute each query individually struggle to provide timely response ...

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 ...

Analytical applications, such as exploratory data analysis and decision support, process complex workloads that include sequences of inter-dependent queries. While modern OLAP systems exploit data parallelism, dependencies force execution ordering constrai ...

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 ...

Modern big data systems process not only a huge volume of data, but also numerous concurrent queries. These queries usually span over distributed data and need to be processed within a strict deadline (e.g.: report generation, SLA, etc.). While users expec ...

Enterprises collect data in large volumes and leverage them to drive numerous concurrent decisions and business processes. Their teams deploy multiple applications that often operate concurrently on the same data and infrastructure but have widely differen ...

The growing demand for data-intensive decision support and the migration to multi-tenant infrastructures put databases under the stress of high analytical query load. The requirement for high throughput contradicts the traditional design of query-at-a-time ...

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 ...

Panagiotis Sioulas

Chattez avec Graph Search

Efficient Concurrent Analytical Query Processing using Data and Workload-conscious Sharing

GPU-accelerated data management under the test of time

Accelerating Complex Analytics using Speculation

Hardware-conscious Query Processing in GPU-accelerated Analytical Engines

Optimizing Goodput through Sharing for Batch Analytics with Deadlines

Oligolithic Cross-task Optimizations across Isolated Workloads

Scalable Multi-Query Execution using Reinforcement Learning

Hardware-conscious Hash-Joins on GPUs