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Jeffrey A. F. Hittinger

dblp:98/9750 · also Jeffrey Hittinger · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2018
0000-0002-8229-6516ORCID · verified

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

Systems, architecture and hardware · 2

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
2 papers
High-performance computing · 52% Parallel and multicore computing · 48%
Software engineering, system software, and programming languages
1 paper
Compilers and program optimization · 100%

Topics — the 7 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Compilers and program optimization
automatic differentiation
0.312018
ADAPT: algorithmic differentiation applied to floating-point precision tuning · SC 2018
Compilers and program optimization › approximate computing › precision tuning
floating-point precision tuning
0.312018
ADAPT: algorithmic differentiation applied to floating-point precision tuning · SC 2018
Parallel and multicore computing › locality optimization
data locality optimization
0.212014
A Study on Balancing Parallelism, Data Locality, and Recomputation in Existing PDE Solvers · SC 2014
Parallel and multicore computing
parallel programming models
0.212014
A Study on Balancing Parallelism, Data Locality, and Recomputation in Existing PDE Solvers · SC 2014
High-performance computing › scientific computing systems
partial differential equation solver
0.212014
A Study on Balancing Parallelism, Data Locality, and Recomputation in Existing PDE Solvers · SC 2014
High-performance computing
scientific computing systems
0.212014
A Study on Balancing Parallelism, Data Locality, and Recomputation in Existing PDE Solvers · SC 2014
Parallel and multicore computing › parallel architecture
NUMA multicore
0.112014
A Study on Balancing Parallelism, Data Locality, and Recomputation in Existing PDE Solvers · SC 2014

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

algorithmic differentiation · 0.7loop fusion · 0.2inter-loop optimization · 0.2communication avoidance · 0.2
YearPublicationVenuePosition
2018 ADAPT: algorithmic differentiation applied to floating-point precision tuning
Harshitha Menon, Michael O. Lam, Daniel Osei-Kuffuor, Markus Schordan, Kathryn Mohror, Jeffrey A. F. Hittinger
SC7
2014 A Study on Balancing Parallelism, Data Locality, and Recomputation in Existing PDE Solvers
abstract
Structured-grid PDE solver frameworks parallelize over boxes, which are rectangular domains of cells or faces in a structured grid. In the Chombo framework, the box sizes are typically 163 or 323, but larger box sizes such as 1283 would result in less surface area and therefore less storage, copying, and/or ghost cells communication overhead. Unfortunately, current on node parallelization schemes perform poorly for these larger box sizes. In this paper, we investigate 30 different inter-loop optimization strategies and demonstrate the parallel scaling advantages of some of these variants on NUMA multicore nodes. Shifted, fused, and communication-avoiding variants for 1283 boxes result in close to ideal parallel scaling and come close to matching the performance of 163 boxes on three different multicore systems for a benchmark that is a proxy for program idioms found in Computational Fluid Dynamic (CFD) codes.
Catherine Mills Olschanowsky, Michelle Mills Strout, Stephen M. Guzik, John Loffeld, Jeffrey A. F. Hittinger
SC5