Ian Glen Neal

I am a third year PhD student studying Computer Science at the University of Michigan. I am advised by Professor Baris Kasikci.

In my research, I want to eliminate the intellectual barrier-to-entry for developers who want to use emerging hardware to develop efficient and reliable systems. To this end, I aim to create tools which developers can use to automatically reason about the characteristics of new hardware so that they can better leverage its capabilities without sacrificing the correctness of their applications. My current focus is in the development of efficient and reliable systems using emerging persistent main memory technologies. I am also interested in developing verifiably secure hardware systems and tools which allow for easier development of secure systems.

News

Jan 9, 2021	Agamotto has been recognized with an IEEE Micro Top Picks Honorable Mention!
Dec 21, 2020	I accepted an invitation to serve as a member of JSys’s Student Editorial Board!
Dec 11, 2020	My publication, “Rethinking File Mapping Structures for Persistent Memory”, has been accepted to FAST ‘21!
Nov 18, 2020	Hippocrates has been accepted to ASPLOS ‘21!
Aug 15, 2020	Agamotto has been accepted to OSDI ‘20!

Selected Publications

ASPLOS’21

HIPPOCRATES: Healing Persistent Memory Bugs Without Doing Any Harm

Ian Neal, Andrew Quinn, and Baris Kasikci

April 2021

Extended Abs (PDF) PDF Url
FAST’21

Rethinking File Mapping for Persistent Memory

Ian Neal, Gefei Zuo, Eric Shiple, Tanvir Ahmed Khan, Youngjin Kwon, Simon Peter, and Baris Kasikci

February 2021

Url
OSDI’20

AGAMOTTO: How Persistent is your Persistent Memory Application?

Ian Neal, Ben Reeves, Ben Stoler, Andrew Quinn, Youngjin Kwon, Simon Peter, and Baris Kasikci

November 2020

Abs PDF Url

Awarded IEEE Micro Top Picks 2021 Honorable Mention!

Persistent Memory (PM) can be used by applications to directly and quickly persist any data structure, without the overhead of a file system. However, writing PM applications that are simultaneously correct and efficient is challenging. As a result, PM applications contain correctness and performance bugs. Prior work on testing PM systems has low bug coverage as it relies primarily on extensive test cases and developer annotations. In this paper we aim to build a system for more thoroughly testing PM applications. We inform our design using a detailed study of 63 bugs from popular PM projects. We identify two application-independent patterns of PM misuse which account for the majority of bugs in our study and can be detected automatically. The remaining application-specific bugs can be detected using compact custom oracles provided by developers. We then present AGAMOTTO, a generic and extensible system for discovering misuse of persistent memory in PM applications. Unlike existing tools that rely on extensive test cases or annotations, AGAMOTTO symbolically executes PM systems to discover bugs. AGAMOTTO introduces a new symbolic memory model that is able to represent whether or not PM state has been made persistent. AGAMOTTO uses a state space exploration algorithm, which drives symbolic execution towards program locations that are susceptible to persistency bugs. AGAMOTTO has so far identified 84 new bugs in 5 different PM applications and frameworks while incurring no false positives.

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