Duke Hound
Statistical Learning for Straggler Diagnosis
Statistical machine learning framwork for
diagnosing performance stragglers from
datacenter traces. Analysis is deployed atop
Spark for distributed computation. Analysis is
demonstrated for production Google datacenter
and Lenovo experimental system.
[Zip]
Hound Repository
The analysis framework accompanies the paper
"Hound: Causal learning for datacenter-scale
straggler diagnosis" in the Proceedings of the
International Conference on Measurement and
Modeling of Computer Systems
[SIGMETRICS'18].
Duke ActionBench
Mobile Benchmarks for Gem5
ActionBench provides APK files of the mobile
benchmarks in an ISPASS 2016 paper. These files
can be placed in a mounted Gem5 image and can be
installed inside the simulator. The repository
includes benchmark source code, written in Java,
and Gem5 simulation scripts.
[GitHub]
ActionBench Repository
[Zip]
ActionBench Repository
The benchmark suite accompanies the paper
"Evaluating Asymmetric Multiprocessing for Mobile
Applications" in the proceedings of the
International Symposium on Performance Analysis of
Systems and Software
[ISPASS'16].
Duke DSM
Datacenter Simulation Methodologies
DSM is a tutorial on datacenter simulation
methodologies. In an era of big data, datacenters
comprise the essential infrastructure for cloud
computing. Yet simulation and evaluation
methodologies remain a challenge as computer
architects seek to improve datacenter performance
and efficiency. This tutorial demonstrates the
tools for datacenter research and walks
partcipants through the approach taken at
Duke. At the end of the tutorial,
participants will be able to (1) deploy a
full-system, cycle-accurate simulator, (2)
simulate datacenter workloads, and (3) explore new
design spaces.
[Website]
ISCA 2015 Tutorial Webpage
[Website]
MICRO 2014 Tutorial Webpage
[Lecture]
Datacenter design and management
Harvard CORE
Comprehensive Optimization via Regression Estimates
CORE is a collection of example R scripts that
construct microarchitectural performance and power
regression models. These models are based on
restricted cubic splines. The derivation process
includes correlation, association, clustering, and
significance analyses. The current scripts
illustrate model construction for out-of-order,
superscalar architectures using data from the IBM
Turandot/PowerTimer simulation infrastructure,
which simulates a POWER4/5-like
architecture.
The code implements statistical techniques for
exploratory data analysis (correlation,
association, clustering, significance testing)
using the open source R statistical computing
package. For downloads and installation
instructions of the R package, refer to its
website.
[Download]
Data for formulating regression models
[Download]
Code for data analysis and regression modeling
[Website]
The R Project for Statistical Computing
This data and code are to accompany the paper "Accurate
and efficient regression modeling for microarchitectural
performance and power prediction" in the proceedings of the
International Conference on Architectural Support for
Programming Languages and Operating Systems
[ASPLOS'06] .
For a detailed explanation of the code and analysis, please refer
to the following tutorial
[IEEE'07].
Further information may be found in the technical report leading
up to the ASPLOS 2006 paper [TR'06].
Berkeley OSKI
Optimized Sparse Kernel Interface
The Optimized Sparse Kernel Interface (OSKI) Library is a
collection of low-level C primitives that provide
automatically tuned computational kernels on sparse matrices,
for use in solver libraries and applications. OSKI has a
BLAS-style interface, providing basic kernels like sparse
matrix-vector multiply and sparse triangular solve, among
others.
[Website]
BeBOP Optimized Sparse Kernel Interface
