# Slashing the disaggregation tax in heterogeneous data centers with FractOS

## Meta Info

Presented in [EuroSys 2022](https://doi.org/10.1145/3492321.3519569).

Homepage:: <https://lsds.doc.ic.ac.uk/projects/fractos>

## Understanding the paper

### TL;DR

This paper presents **FractOS**, a distributed OS that is designed to minimize the network overheads of disaggregation in heterogeneous data centers.

It enables *direct P2P data transfers between different devices*, without centralized application and OS control.

### Existing problem

* *Current software stacks introduce unnecessary messages* through the shared data-center network in a disaggregated system.

### How to manage accelerators (GPUs)

* Compared to rCUDA
  * rCUDA accesses remote GPUs transparently by *interposing CUDA driver calls*.
  * FractOS GPU service uses *a single roundtrip Request invocation* per kernel invocation.
* FractOS
  * Build *a GPU adaptor* to *expose* a disaggregated GPU.
  * The GPU adaptor *runs on the host CPU*, *using the OS GPU driver*, and *offers several RPCs* exposed through *Requests*: GPU context initialization, memory de/allocation, kernel loading, kernel invocation, and cleanup.

### Implementation

* 17.5K LoC of C++.