Publications associées à Hardware-assisted virtualization

CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds

David Atienza Alonso, Marina Zapater Sancho, Luis Maria Costero Valero, Darong Huang, Ali Pahlevan

Computing servers have played a key role in developing and processing emerging compute-intensive applications in recent years. Consolidating multiple virtual machines (VMs) inside one server to run various applications introduces severe competence for limi ...

2024

A RISC-V Extension to Minimize Privileges of Enclave Runtimes

Edouard Bugnion, Neelu Shivprakash Kalani

In confidential computing, the view of the system software is Manichean: the host operating system is untrusted and the TEE runtime system is fully trusted. However, the runtime system is often as complex as a full operating system, and thus is not free fr ...

ACM2023

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

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

Design of a Collimation Section for the FCC-ee

Tatiana Pieloni, Bruce Roderik, Frank Zimmermann

The design parameters of the FCC-ee foresee operation with a total stored beam energy of about 20 MJ, exceeding those of previous lepton colliders by almost two orders of magnitude. Given the inherent damage potential, a halo collimation system is studied ...

JACoW Publishing2022

Performance Characterization of HTAP Workloads

Anastasia Ailamaki, Utku Sirin, Sandhya Dwarkadas

Hybrid Transactional and Analytical Processing (HTAP) systems have become popular in the past decade. HTAP systems allow running transactional and analytical processing workloads on the same data and hardware. As a result, they suffer from workload interfe ...

IEEE COMPUTER SOC2021

Rebooting Virtual Memory with Midgard

Babak Falsafi, Mathias Josef Payer, Siddharth Gupta, Atri Bhattacharyya, Yunho Oh, Abhishek Bhattacharjee

Computer systems designers are building cache hierarchies with higher capacity to capture the ever-increasing working sets of modern workloads. Cache hierarchies with higher capacity improve system performance but shift the performance bottleneck to addres ...

2021

JIT works: decide when all data is known (VLDB Women in Database Research Award talk)

Anastasia Ailamaki

When the time comes to make a critical decision, it is of paramount importance to prepare enough so that all the information necessary is available at decision time. Under-preparation leads to uninformed decisions; over-preparation, however, may lead to co ...

ASSOC COMPUTING MACHINERY2020

Lupulus: A Flexible Hardware Accelerator For Neural Networks

Andreas Peter Burg, Alexios Konstantinos Balatsoukas Stimming, Robert Giterman, Andreas Toftegaard Kristensen

Neural networks have become indispensable for a wide range of applications, but they suffer from high computational- and memory-requirements, requiring optimizations from the algorithmic description of the network to the hardware implementation. Moreover, ...

IEEE2020

Micro-architectural Analysis of OLAP: Limitations and Opportunities

Anastasia Ailamaki, Utku Sirin

Understanding micro-architectural behavior is important for efficiently using hardware resources. Recent work has shown that in-memory online transaction processing (OLTP) systems severely underutilize their core micro-architecture resources [29]. Whereas, ...

ASSOC COMPUTING MACHINERY2020