Publications associées à A Data-oriented Transaction Execution Engine and Supporting Tools

Oligolithic Cross-task Optimizations across Isolated Workloads

Anastasia Ailamaki, Panagiotis Sioulas, Eleni Zapridou

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 ...

2024

Highly Parallel RTL Simulation

Verification and testing of hardware heavily relies on cycle-accurate simulation of RTL. As single-processor performance is growing only slowly, conventional, single-threaded RTL simulation is becoming impractical for increasingly complex chip designs and ...

EPFL2024

EdgeAI-Aware Design of In-Memory Computing Architectures

Marco Antonio Rios

Driven by the demand for real-time processing and the need to minimize latency in AI algorithms, edge computing has experienced remarkable progress. Decision-making AI applications stand out for their heavy reliance on data-centric operations, predominantl ...

EPFL2024

Post-Moore's Law Fusion: High-Bandwidth Memory, Accelerators, and Native Half-Precision Processing for CPU-Local Analytics

Anastasia Ailamaki, Viktor Sanca

Modern data management systems aim to provide both cutting-edge functionality and hardware efficiency. With the advent of AI-driven data processing and the post-Moore Law era, traditional memory-bound scale-up data management operations face scalability ch ...

2023

Chaosity: Understanding Contemporary NUMA-architectures

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

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

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

Building Chips Faster: Hardware-Compiler Co-Design for Accelerated RTL Simulation

Sahand Kashani

The demise of Moore's Law and Dennard scaling has resulted in diminishing performance gains for general-purpose processors, and so has prompted a surge in academic and commercial interest for hardware accelerators. Specialized hardware has already redefine ...

EPFL2023

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

Acceleration of graph pattern mining and applications to financial crime

Jovan Blanusa

Various forms of real-world data, such as social, financial, and biological networks, can be represented using graphs. An efficient method of analysing this type of data is to extract subgraph patterns, such as cliques, cycles, and motifs, from graphs. For ...

EPFL2023

Determining an optimum quantity of interleaved instruction streams of defined coroutines

Anastasia Ailamaki, Georgios Psaropoulos

A method for optimizing memory access for database operations is provided. The method may include performing a first database operation by at least executing a first instruction stream associated with the first database operation. The first database opera ...

2023

Acceleration of Control Intensive Applications on Coarse-Grained Reconfigurable Arrays for Embedded Systems

David Atienza Alonso, Benoît Walter Denkinger, Miguel Peon Quiros

Embedded systems confront two opposite goals: low-power operation and high performance. The current trend to reach these goals is toward heterogeneous platforms, including multi-core architectures with heterogeneous cores and hardware accelerators. The lat ...

2023

TiC-SAT: Tightly-coupled Systolic Accelerator for Transformers

David Atienza Alonso, Giovanni Ansaloni, Alireza Amirshahi, Joshua Alexander Harrison Klein

Transformer models have achieved impressive results in various AI scenarios, ranging from vision to natural language processing. However, their computational complexity and their vast number of parameters hinder their implementations on resource-constraine ...

2023

Inter-actions parallel execution on GPU from high-level dataflow synthesis

Marco Mattavelli, Simone Casale Brunet, Aurélien François Gilbert Bloch

Recent GPU architectures make available numbers of parallel processing units that exceed by orders of magnitude the ones offered by CPU architectures. Whereas programs written using dataflow programming languages are well suited for programming heterogeneo ...

IEEE2022

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

VWR2A: A Very-Wide-Register Reconfigurable-Array Architecture for Low-Power Embedded Devices

David Atienza Alonso, Benoît Walter Denkinger, Miguel Peon Quiros

Edge-computing requires high-performance energy-efficient embedded systems. Fixed-function or custom accelerators, such as FFT or FIR filter engines, are very efficient at implementing a particular functionality for a given set of constraints. However, the ...

ASSOC COMPUTING MACHINERY2022

DFAulted: Analyzing and Exploiting CPU Software Faults Caused by FPGA-Driven Undervolting Attacks

Mirjana Stojilovic, David Dervishi, Dina Gamaleldin Ahmed Shawky Mahmoud

Field-programmable gate arrays (FPGAs) combine hardware reconfigurability with a high degree of parallelism. Consequently, FPGAs offer performance gains and power savings for many applications. A recent trend has been to leverage the hardware versatility o ...

2022

Robustness Against Read Committed: A Free Transactional Lunch

Christoph Koch, Bas Ketsman

Transaction processing is a central part of most database applications. While serializability remains the gold standard for desirable transactional semantics, many database systems offer improved transaction throughput at the expense of introducing potenti ...

ASSOC COMPUTING MACHINERY2022

Decentralized in-order execution of a sequential task-based code for shared-memory architectures

Charly Nicolas Lucien Castes

The hardware complexity of modern machines makes the design of adequate programming models crucial for jointly ensuring performance, portability, and productivity in high-performance computing (HPC). Sequential task-based programming models paired with adv ...

IEEE COMPUTER SOC2022

Deciding Robustness for Lower SQL Isolation Levels

Christoph Koch, Bas Ketsman

While serializability always guarantees application correctness, lower isolation levels can be chosen to improve transaction throughput at the risk of introducing certain anomalies. A set of transactions is robust against a given isolation level if every p ...

ASSOC COMPUTING MACHINERY2022

Structured and tiled-based pruning of Deep Learning models targeting FPGA implementations

Alexandre Schmid, Lizeth Gonzalez Carabarin

Model compression techniques have lead to a reduction of size and number of computations of Deep Learning models. However, techniques such as pruning mostly lack of a real co-optimization with hardware platforms. For instance, implementing unstructured pru ...

IEEE2022