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Matt Humphrey

dblp:175/6153 · DBLP profile ↗
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4ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0006-5339-1297ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 2Computer networks · 1Software engineering, systems software and programming languages · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
4 papers
Electronic design automation · 36% Cloud and datacenter computing · 35% Integrated circuit design · 18%
Computer networks
1 paper
Datacenter networks · 100%

Topics — the 10 heaviest of 13, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Electronic design automation
hardware description language
0.812024
Wavefront Threading Enables Effective High-Level Synthesis · Proc. ACM Program. Lang. 2024
Electronic design automation
high-level synthesis
0.812024
Wavefront Threading Enables Effective High-Level Synthesis · Proc. ACM Program. Lang. 2024
Integrated circuit design › digital system design
register-transfer level design
0.812024
Wavefront Threading Enables Effective High-Level Synthesis · Proc. ACM Program. Lang. 2024
Cloud and datacenter computing › virtualization
network virtualization
0.312018
Azure Accelerated Networking: SmartNICs in the Public Cloud · NSDI 2018
Cloud and datacenter computing › computation offloading › network function offloading
SmartNIC offload
0.312018
Azure Accelerated Networking: SmartNICs in the Public Cloud · NSDI 2018
Cloud and datacenter computing
virtualization
0.312018
Azure Accelerated Networking: SmartNICs in the Public Cloud · NSDI 2018
Cloud and datacenter computing › datacenter architecture
datacenter acceleration
0.212016
Agile Co-Design for a Reconfigurable Datacenter · FPGA 2016
Reconfigurable computing and FPGAs
FPGA accelerator
0.212016
A cloud-scale acceleration architecture · MICRO 2016
Hardware accelerators and domain-specific architectures
network accelerator
0.112018
Azure Accelerated Networking: SmartNICs in the Public Cloud · NSDI 2018
Reconfigurable computing and FPGAs
datacenter FPGA deployment
0.112016
Agile Co-Design for a Reconfigurable Datacenter · FPGA 2016

Methods — techniques the papers use, named apart from their topics

high-level synthesis · 0.8execution model · 0.8measurement · 0.5deployment · 0.5hardware-software co-design · 0.2agile development · 0.2
YearPublicationVenuePosition
2024 Wavefront Threading Enables Effective High-Level Synthesis
abstract
Digital systems are growing in importance and computing hardware is growing more heterogeneous. Hardware design, however, remains laborious and expensive, in part due to the limitations of conventional hardware description languages (HDLs) like VHDL and Verilog. A longstanding research goal has been programming hardware like software, with high-level languages that can generate efficient hardware designs. This paper describes Kanagawa, a language that takes a new approach to combine the programmer productivity benefits of traditional High-Level Synthesis (HLS) approaches with the expressibility and hardware efficiency of Register-Transfer Level (RTL) design. The language’s concise syntax, matched with a hardware design-friendly execution model, permits a relatively simple toolchain to map high-level code into efficient hardware implementations.
Blake Pelton, Adam Sapek, Kenneth Eguro, Daniel Lo, Alessandro Forin, Matt Humphrey, Jinwen Xi, Rajas Karandikar, Johannes de Fine Licht, Evgeny Babin, Adrian M. Caulfield, Doug Burger
Proc. ACM Program. Lang.6
2018 Azure Accelerated Networking: SmartNICs in the Public Cloud
Daniel Firestone, Andrew Putnam, Sambrama Mundkur, Derek Chiou, Alireza Dabagh, Mike Andrewartha, Hari Angepat, Vivek Bhanu, Adrian M. Caulfield, Eric S. Chung, Harish Kumar Chandrappa, Somesh Chaturmohta, Matt Humphrey, Jack Lavier, Norman Lam, Fengfen Liu, Kalin Ovtcharov, Jitendra Padhye, Gautham Popuri, Shachar Raindel, Tejas Sapre, Mark Shaw 0001, Gabriel Silva, Madhan Sivakumar, Nisheeth Srivastava, Anshuman Verma, Qasim Zuhair, Deepak Bansal, Doug Burger, Kushagra Vaid, David A. Maltz, Albert G. Greenberg
NSDI13
2016 Agile Co-Design for a Reconfigurable Datacenter
abstract
In 2015, a team of software and hardware developers at Microsoft shipped the world?s first commercial search engine accelerated using FPGAs in the datacenter. During the sprint to production, new algorithms in the Bing ranking service were ported into FPGAs and deployed to a production bed within several weeks of conception, leading to significant gains in latency and throughput. The fast turnaround time of new features demanded by an agile software culture would not have been possible without a disciplined and effective approach to co-design in the datacenter. This talk will describe some of the learnings and best practices developed from this unique experience.
Shlomi Alkalay, Hari Angepat, Adrian M. Caulfield, Eric S. Chung, Oren Firestein, Michael Haselman, Stephen Heil, Kyle Holohan, Matt Humphrey, Tamás Juhász, Puneet Kaur, Sitaram Lanka, Daniel Lo, Todd Massengill, Kalin Ovtcharov, Michael Papamichael, Andrew Putnam, Raja Seera, Rimon Tadros, Jason Thong, Lisa Woods, Derek Chiou, Doug Burger
FPGA9
2016 A cloud-scale acceleration architecture
abstract
Hyperscale datacenter providers have struggled to balance the growing need for specialized hardware (efficiency) with the economic benefits of homogeneity (manageability). In this paper we propose a new cloud architecture that uses reconfigurable logic to accelerate both network plane functions and applications. This Configurable Cloud architecture places a layer of reconfigurable logic (FPGAs) between the network switches and the servers, enabling network flows to be programmably transformed at line rate, enabling acceleration of local applications running on the server, and enabling the FPGAs to communicate directly, at datacenter scale, to harvest remote FPGAs unused by their local servers. We deployed this design over a production server bed, and show how it can be used for both service acceleration (Web search ranking) and network acceleration (encryption of data in transit at high-speeds). This architecture is much more scalable than prior work which used secondary rack-scale networks for inter-FPGA communication. By coupling to the network plane, direct FPGA-to-FPGA messages can be achieved at comparable latency to previous work, without the secondary network. Additionally, the scale of direct inter-FPGA messaging is much larger. The average round-trip latencies observed in our measurements among 24, 1000, and 250,000 machines are under 3, 9, and 20 microseconds, respectively. The Configurable Cloud architecture has been deployed at hyperscale in Microsoft's production datacenters worldwide.
Adrian M. Caulfield, Eric S. Chung, Andrew Putnam, Hari Angepat, Jeremy Fowers, Michael Haselman, Stephen Heil, Matt Humphrey, Puneet Kaur, Joo-Young Kim 0001, Daniel Lo, Todd Massengill, Kalin Ovtcharov, Michael Papamichael, Lisa Woods, Sitaram Lanka, Derek Chiou, Doug Burger
MICRO8