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

Related publications (80)

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Non-Volatile Memory (NVM) is an emerging type of memory device that provides fast, byte-addressable, and high-capacity durable storage. NVM sits on the memory bus and allows durable data structures designs similar to the in-memory equivalent ones. Expensiv ...

EPFL2021

Micro-architectural Analysis of Database Workloads

Utku Sirin

Database workloads have significantly evolved in the past twenty years. Traditional database systems that are mainly used to serve Online Transactional Processing (OLTP) workloads evolved into specialized database systems that are optimized for particular ...

EPFL2021

NVCache: A Plug-and-Play NVMM-based I/O Booster for Legacy Systems

Rafael Pereira Pires, Pascal Felber

This paper introduces NVCache, an approach that uses a non-volatile main memory (NVMM) as a write cache to improve the write performance of legacy applications. We compare NVCache against file systems tailored for NVMM (Ext4-DAX and NOVA) and with I/O-heav ...

IEEE2021

Distributed Computing with Modern Shared Memory

Mihail Igor Zablotchi

In this thesis, we revisit classic problems in shared-memory distributed computing through the lenses of (1) emerging hardware technologies and (2) changing requirements. Our contributions consist, on the one hand, in providing a better understanding of th ...

EPFL2020

GPU-accelerated data management under the test of time

Anastasia Ailamaki, Periklis Chrysogelos, Angelos Christos Anadiotis, Panagiotis Sioulas, 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

SPARTA: A Divide and Conquer Approach to Address Translation for Accelerators

Babak Falsafi, Javier Picorel Obando, Abhishek Bhattacharjee

Virtual memory (VM) is critical to the usability and programmability of hardware accelerators. Unfortunately, implementing accelerator VM efficiently is challenging because the area and power constraints make it difficult to employ the large multi-level TL ...

2020

In order to get a better understanding of the interaction of plasma microinstabilities and associated turbulence with specific modes, an antenna is implemented in the global gyrokinetic Particle-In-Cell (PIC) code ORB5. It consists in applying an external ...

EPFL2020

A review of CUDA optimization techniques and tools for structured grid computing

Recent advances in GPUs opened a new opportunity in harnessing their computing power for general purpose computing. CUDA, an extension to C programming, is developed for programming NVIDIA GPUs. However, efficiently programming GPUs using CUDA is very tedi ...

2020

Accurate deep neural network inference using computational phase-change memory

Irem Boybat Kara, Evangelos Eleftheriou, Abu Sebastian

In-memory computing using resistive memory devices is a promising non-von Neumann approach for making energy-efficient deep learning inference hardware. However, due to device variability and noise, the network needs to be trained in a specific way so that ...

2020

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Area and power constrained edge devices are increasingly utilized to perform compute intensive workloads, necessitating increasingly area and power efficient accelerators. In this context, in-SRAM computing performs hundreds of parallel operations on spati ...

2020