Periklis Chrysogelos

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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 ...

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 ...

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 ...

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, ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Periklis Chrysogelos

Chattez avec Graph Search

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

Efficient Analytical Query Processing on CPU-GPU Hardware Platforms

LAQy: Efficient and Reusable Query Approximations via Lazy Sampling

Adaptive HTAP through Elastic Resource Scheduling

Boosting Efficiency of External Pipelines by Blurring Application Boundaries

GPU-accelerated data management under the test of time

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

Hardware-conscious Query Processing in GPU-accelerated Analytical Engines

Adaptive HTAP through Elastic Resource Scheduling

Hardware-conscious Hash-Joins on GPUs

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

Efficient and Reusable Lazy Sampling