Transcript www.cs.utep.edu

University of Texas-El Paso
Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
Dynamic Adaptivity
in Support of Extreme Scale
Pat Teller, UTEP
9 June 2005
FastOS PI Meeting
1
University of Texas-El Paso
Dynamic Adaptability in Support of Extreme Scale
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9 June 2005
University of Wisconsin-Madison
Outline
Collaborators
Overview
Progress
Plans
FastOS PI Meeting
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Partners
Dynamic Adaptability in Support of Extreme Scale
University of Texas-El Paso
University of Wisconsin-Madison
University of Texas at El Paso
Department of Computer Science
Patricia J. Teller ([email protected])
University of Wisconsin — Madison
Computer Sciences Department
Barton P. Miller ([email protected])
International Business Machines, Inc.
Linux Technologies Center
Bill Buros ([email protected])
new partner
9 June 2005
Lawrence Berkeley National Laboratory
Leonid Oliker ([email protected])
U.S. Department of Energy
Office of Science
Fred Johnson ([email protected])
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Teams
Dynamic Adaptability in Support of Extreme Scale
University of Texas-El Paso
University of Wisconsin-Madison
• UTEP team (Pat Teller)
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Rodrigo Romero, Ph.D. (Post-doc)
Seetharami Seelam, Ph.D. candidate in CS
Luis Ortiz, Ph.D. candidate in CS
Jayaraman Suresh, Master’s candidate in CS
Brenda Prieto, Master’s candidate in ECE
Nidia Pedregon, Undergraduate in CS
Alejandro Castaneda, Undergraduate in CS
• U. Wisconsin-Madison team (Bart Miller)
– Michael Brim, Ph.D. candidate
– Igor Grobman, Ph.D. candidate
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University of Texas-El Paso
Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
Outline
• Collaborators
• Overview
– Goal
– Challenges
– Deliverables
– Methodology
• Progress & Direction
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University of Texas-El Paso
Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
Goal: Enhanced Performance
Generalized
Customized
resource management
Fixed
9 June 2005
Dynamically Adaptable
OS/runtime services
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University of Texas-El Paso
Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
Challenges
Determining
• What to adapt
• When to adapt
• How to adapt
• How to measure effects of adaptation
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Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
Deliverables
1. Mechanisms to dynamically sense,
analyze, and adjust
• performance metrics
• fluctuating workload situations
• overall system environment
conditions
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Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
Deliverables
2. Linux prototypes and experiments
that demonstrate dynamic self-tuning /
provisioning in HPC environments
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University of Texas-El Paso
Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
Deliverables
3. Methodology for general-purpose OS
adaptation
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Methodology University of Texas-El Paso
University of Wisconsin-Madison
Dynamic Adaptability in Support of Extreme Scale
identify adaptation
targets
characterize workload
resource usage patterns
potentially profitable adaptation targets
off line
determine/redetermine feasible adaptation ranges
off line/
run time
define/adapt metrics/heuristics
to trigger adaptation
generate/adapt monitoring, triggering and
adaptation code, and attach it to OS
monitor application execution,
assessing performance (gain) and
triggering adaptation as necessary
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FastOS PI Meeting
KernInst
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KernInst
University of Texas-El Paso
University of Wisconsin-Madison
Dynamic Adaptability in Support of Extreme Scale
dynamic monitoring, instrumentation,
and adaptation of the kernel
IBM pSeries
eServer 690
Client
KernInst Daemon
Instrumentation
Tool
KernInst Device
KernInst API
9 June 2005
Linux Kernel
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University of Texas-El Paso
Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
Outline
• Collaborators
• Overview
• Progress & Direction
– Tools
– Infrastructure
– Collaboration
– Research
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University of Texas-El Paso
Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
Tools Progress
• KernInst –
– POWER4 port for Linux 2.4 and IA32 Linux 2.6
• Modifications/Enhancements for DAiSES research
– POWER4 port for Linux 2.6 underway
• IOstat (coarse statistics) – POWER4 Linux
2.6
• Investigating complementary use of oprofile
and kprobes – POWER4 Linux 2.6
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Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
KernInst
• Intel IA32 port for Linux 2.4 and 2.6 for Pentium
•
3 and Pentium 4 processors
IBM POWER4 port for Linux 2.4
• Supports stand-alone kernels and kernels that
run under the Hypervisor virtual machine layer
• Hypervisor layer not transparent and requires
explicit support
• Little public documentation on this layer
available
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Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
Outline
• Collaborators
• Overview
• Progress & Direction
– Tools
– Infrastructure
– Collaboration
– Research
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University of Texas-El Paso
Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
Infrastructure Progress
IBM SUR Grants, UTEP Star Award, UTEP PUF Funds
• Development of Experimental Platforms at UTEP
– IBM eServer pSeries 690 (16 processors, 32GB, 2TB)
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Linux 2.4/2.6 partition for KernInst development
Linux 2.4 partition for DAiSES research
Linux 2.6 partition for DAiSES research
DS4300 RAID for DAiSES I/O-related research (1TB)
– Xeon workstations – Linux 2.4 and 2.6
– IBM eServer p590 (24 processors, 64GB, 2TB)
– IBM eServer p550 (4 processors, )
• Establishment of DAiSES Lab at UTEP
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Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
Outline
• Collaborators
• Overview
• Progress & Direction
– Tools
– Infrastructure
– Collaboration
– Research
9 June 2005
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University of Texas-El Paso
Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
Build/Strengthen Collaborations - 1
• April 14-16, 2004 – Seetharami Seelam, UTEP, attended
Paradyn/Condor week
• October 24, 2004 – Barney MacCabe, UNM, visited UTEP to meet
with the DAiSES team and give a talk re: his team’s FASTOS
research
• November 10, 2004 – Pat Teller, UTEP, participated in the FASTOS
Birds-of-a-Feather meeting at SC2004 – this was the first public
presentation of the DAiSES project
• November 2004 – Rodrigo Romero, Seetharami Seelam, and Pat
Teller, UTEP, and Michael Brim, Igor Grobman, and Bart Miller, UWMadison, promoted the DAiSES project at two SC2004 research
exhibits, one shared by UTEP, UNM, New Mexico State University,
and New Mexico Institute of Technology, and another of UWMadison
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Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
Build/Strengthen Collaborations - 2
• February 25, 2005 – Luis Ortiz, Rodrigo Romero, Seetharami
Seelam, and Pat Teller, UTEP, attended a half-day meeting at IBMAustin with approximately nine members of the Linux Technologies
team
• March 3, 2005 –Rodrigo Romero, Seetharami Seelam, and Pat
Teller, UTEP, attended an all-day meeting at UNM with Barney
Maccabe, Patrick Bridges, Kurt Ferreira, an Edgar Leon, UNM/CS,
Orran Krieger, IBM, Ron Brightwell and Rolf Riesen, SNL, and Rod
Oldehoeft, LANL/ACL
• March 20-24, 2005 – Igor Grobman, UW-Madison, visited UTEP
and led a workshop re: the use of KernInst and Kperfmon to
implement adaptations, in particular, in the process scheduler and
I/O scheduler; Bill Buros, IBM-Austin, senior member of Linux
Technologies team, attended for three days – resulted in
modifications/enhancements to KernInst
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Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
Build/Strengthen Collaborations - 3
• May 4, 2005 – after IBM Petaflops Tools Strategy Workshop at IBM
TJ Watson Research Center, Bart Miller, UW-Madison, and
Seetharami Seelam (awarded $500 to attend the workshop) and Pat
Teller, UTEP, met with Evelyn Duesterwald and Robert Wisniewski
re: possible collaborations
• Weekly telecons with IBM-Austin team
• Telecons and Access Grid meetings with UW-Madison team
• Shared Enotebook to be launched shortly
• Shared data repository with search tool to be launched shortly
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Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
Outline
• Collaborators
• Overview
• Progress & Direction
– Tools
– Infrastructure
– Collaboration
– Research
9 June 2005
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University of Texas-El Paso
Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
Current Research Thrusts
• Dynamic Code Optimization
• Low-hanging Fruit (i.e., opportunitistic
targets of adaptation)
• Identification of Adaptation Targets via Selfpropelled Instrumentation
• Other Directions
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Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
Dynamic Code Optimization
• Investigation of dynamic code optimization strategies
– [Tamches and Miller] used dynamic reorganization of basic block
layout in parts of SPARC Solaris kernel to improve performance
via I-cache miss reduction
• Discussion in research community asks if such
optimizations are
– workload dependent and need to be done
dynamically or
– mostly independent of workload and can be done
statically
• Goal: to provide conclusive evidence either way
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Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
Low-hanging Fruit
I/O scheduler parameter
selection via neural
networks – extend work
of IBM-Austin that uses
genetic algorithms
virtual memory management –
extend dissertation work
9 June 2005
FastOS PI Meeting
I/O scheduling –
extend work of IBMAustin
process scheduling –
extend published
observations and
address daemon
control
page size – extend
work of IBM TJ
Watson Research Ctr.
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Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
I/O Scheduling (in progress)
• What to adapt: I/O scheduler
• Dynamic selection of “appropriate” I/O scheduler for
observed “system state”
• When to adapt
• Change in “system state” (now identified via IOstat)
• Below threshold related to number of queued I/O
requests
• How to adapt
• Linux 2.6 provides capability
• I/O schedulers characterized w.r.t. “best” performance
for different “system states” [Pratt and Heger]
• How to measure effects of adaptation
• Execution time and throughput MB/s (for now)
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University of Wisconsin-Madison
I/O Scheduling By-products - 1
Enhancements to Linux I/O Scheduling,” to appear
in Proceedings of the Linux Symposium, Ottawa,
Canada, July 2005 (S. Seelam, R. Romero, P.
Teller, and W. Buros)
• Reviews previous work of IBM-Austin characterizes
workloads best served by each of the four Linux 2.6 I/O
schedulers (can be selected at boottime or runtime)
• Presents cases where the Anticipatory Scheduler (AS)
results in process starvation
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Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
I/O Scheduling By-products - 1 cont’d.
• Presents and demonstrates performance of UTEP CAS,
Cooperative Anticipatory Scheduler
• extends anticipation to “cooperative” processes that
collectively issue synchronous requests to a close set
of disk blocks
• compares performance to current four schedulers
• shows order of magnitude performance improvement
in cases where AS performs poorly
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Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
I/O Scheduling By-products - 2
In progress: demonstration of heuristically-guided
dynamic selection of Linux 2.6 I/O schedulers
(target: FAST)
• First step towards making I/O scheduling fully autonomic
(Ph.D. dissertation topic: Seetharami Seelam)
• Selection based on observed system behavior, i.e.,
system (workload) I/O behavior, metric, in particular, I/O
request size
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University of Wisconsin-Madison
I/O Scheduling By-products - 2 cont’d.
• Using a priori measurements of disk throughput under
the various schedulers and request sizes to generate a
function that at runtime, given the current average
request size, returns the scheduler that gives the best
measured throughput for the specified disk
• Identying adaptation interval, i.e., when it is not too
expensive to switch schedulers–based on number of
queued I/O requests
• Future work: UW-Madison team will use KernInst to
effect the adaptation
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Overhead of Draining
I/O Queue
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University of Wisconsin-Madison
Dynamic Adaptability in Support of Extreme Scale
14
12
Overhead (1000 ms)
.
Deadline to AS
10
AS to Deadline
8
6
Time to Drain for Default nr_requests
4
2
0
1
2
4
8
16
32
64
128
256
512
1024
Number of Requests
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Microbenchmark
Synchronous
Reads
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University of Wisconsin-Madison
Dynamic Adaptability in Support of Extreme Scale
Comparison of Different I/O Schedulers on RAID-0
.
45
36
CFQ
Bandwidth (MB/s)
AS
Deadline
Points of interests
27
NOOP
18
AS
9
Deadline
CFQ
0
1KB
4KB
16KB
32KB
65KB
256KB
1MB
Read Size
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Microbenchmark
Synchronous
Reads ZOOMED 1
Dynamic Adaptability in Support of Extreme Scale
University of Texas-El Paso
University of Wisconsin-Madison
Comparison of Different I/O Schedulers on RAID-0
6
Bandwidth (MB/s) .
5
4
30% difference between the best and the worst
performing scheduler
CFQ
3
AS
Deadline
NOOP
2
1
1KB
4KB
16KB
32KB
Read Size
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Microbenchmark
Synchronous
Reads ZOOMED 2
Dynamic Adaptability in Support of Extreme Scale
University of Texas-El Paso
University of Wisconsin-Madison
Com parison of Different I/O Schedulers on RAID-0
50
45
Bandwidth (MB/s) .
40
>20% difference betw een the best and the
w orst perform ing scheduler
35
30
25
CFQ
20
AS
15
Deadline
NOOP
10
5
0
32KB
65KB
256KB
1MB
Read Size
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Microbenchmark
Writes
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University of Wisconsin-Madison
Dynamic Adaptability in Support of Extreme Scale
Comparison of Different I/O Schedulers on RAID-0
54
CFQ
AS
45
Deadline
.
NOOP
Bandwidth (MB/s)
36
27
18
9
0
1KB
4KB
16KB
64KB
256KB
1MB
Read Size
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Linux Compilation
Disk Accesses
University of Texas-El Paso
University of Wisconsin-Madison
Dynamic Adaptability in Support of Extreme Scale
Histogram of the Size of Linux Source files
2500
6000
2000
5000
Number of Files .
Number of Files .
Histogram of Size of Linux Object Files
1500
1000
500
3000
2000
1000
32
M
8M
16
M
4M
2M
1M
64
K
12
8K
25
6K
51
2K
32
K
8K
16
K
4K
2K
0
1K
0K
0
4000
0K
1K
2K
4K
8K
16K
32K
64K 128K 256K 512K
1M
2M
Size
Size
Comparison of Different I/O Schedulers on RAID-0
Comparison of Different I/O Schedulers on RAID-0
54
45
CFQ
AS
45
.
.
Deadline
36
CFQ
NOOP
AS
Points of interests
Bandwidth (MB/s)
Bandwidth (MB/s)
36
27
18
Deadline
Points of interests
27
NOOP
18
AS
9
9
Deadline
Deadline
CFQ
CFQ
0
0
1KB
4KB
16KB
65KB
256KB
1MB
1KB
Read Size
9 June 2005
4KB
16KB
32KB
65KB
256KB
1MB
Read Size
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Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
• Dynamic Adaptation
– Uses IOstat information, which is after the fact
– Read/Write prediction uses two-bit saturating
counter
– For reads: size 1K - 32K
use Deadline
size > 32K
– For writes: size 1K - 64K
size > 64K
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FastOS PI Meeting
use AS
use AS
use Noop
37
Linux Compilation
Read/Write Access
Dynamic Adaptability in Support of Extreme Scale
University of Texas-El Paso
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Read/Write Patterns while compiling Linux source
Read/Write
1
0
1
26 51
76 101 126 151 176 201 226 251 276 301 326 351 376 401 426 451 476 501 526 551 576 601 626 651 676 701 726 751 776 801 826 851
Tim e (sec)
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Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
• Performance comparison of Linux Compilation
(gmake –j 16; xeon 2.78GHz; source on RAID-0
with 4 IDE drives)
Scheduler Time (Sec)
AS
830
Deadline
850
preliminary
CFQ
860
results –
inconclusive
Noop
848
Adaptive
843
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University of Texas-El Paso
Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
Lessons Learned
• 80 scheduler switches
• Switching is not “costing our life”
• Switching has to be on a coarser granularity
– “Smaller” picture (focus: requests) is captured
– “Bigger” picture (focus: workloads) needs to be
captured
– Prediction does not have to be per-request
– Example desired prediction: database workload
followed by streaming reads – should select
deadline/cfq followed by AS
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Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
Applications/Benchmarks used for
I/O Scheduling Research - 1
• Flexible File System Benchmark – FFSB bench
– used to generate profile-driven, I/O workload with a
characteristic I/O access pattern of a given type of server, e.g.,
web, file, email servers, and MetaData server.
• Microbenchmarks
– Streaming writes and chunk reads
– Streaming reads and chunk reads
– Chunk reads
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Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
Applications/Benchmarks used for
I/OScheduling Research - 2
•
MADbench: I/O intensive (Borril, et al.)
–
–
based on MADCAP, an application for estimating the power
spectrum of cosmic microwave background radiation
retains computational intensity, operational complexity, and
system requirements of MADCAP and implements its three
main processing steps
1. builds signal correlation derivative matrices and it requires neither
read operations nor communication
2. builds a subset of a data correlation matrix and then inverts it;
does not require writes
3. reads a subset of the signal correlation matrices built in the first
step and performs matrix multiplication against the inverted matrix
obtained in step two; does not require writes
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Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
Process Scheduling (stalled)
Several references indicate that the Linux 2.4
scheduling policy has an adverse effect on nonreal-time, non-interactive applications—a
description that fits the high-performance
applications
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Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
Process Scheduling
• What to adapt: process scheduler
• Dynamic selection of “appropriate” process scheduler
for observed “system state”
• When to adapt
• Change in “system state”
• Below threshold related to time spent in scheduler or
length of “runnable” queue
• How to adapt
• Change process type to real-time, i.e., scheduler to
round-robin
• A priori knowledge of process IDs
• How to measure effects of adaptation
• Execution time
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University of Wisconsin-Madison
Dynamic Adaptability in Support of Extreme Scale
Hackbench 2.4 execution times for
unmodified (blue) and adapted scheduler
160
140
Adaptation: timeshared to
real time (round-robin)
scheduling with fixed Too much
quantum and priority per
time in
process for the lifetime of
scheduler
the application
seconds
120
100
80
2.4 Baseline
2.4 Queue Length
adaptation
60
2.4 Time in sched.
adaptation
40
Adaptation to
round-robin
20
0
20
40
60
80
100
120
140
160
180
200
220
Groups
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Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
Hackbench Speedup w rt 2.4 unm odified kernel
2.4 Q. Length
22
0
18
0
14
0
10
0
2.4 Time in sched.
60
20
3.00
2.50
2.00
1.50
1.00
0.50
0.00
Groups
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Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
Learning-based, Heuristic-driven
Dynamic Adaptation (longer term)
• I/O scheduler parameter selection
• IBM-Austin: genetic algorithms
• UTEP: investigate potentially lower cost hybrid
techniques that combine the use of neural networks,
genetic algorithms, and fuzzy logic (possible dissertation
topic: Luis Ortiz, UTEP)
• Master’s thesis: neural network approach to selecting
parameters of the Anticipatory Scheduler of Linux 2.6
[Moilanen]
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University of Wisconsin-Madison
Near-term Research Directions – 1
• Adaptation target investigation via self-propelled
instrumentation to obtain function-level traces from
applications and the kernel [Mirgorodskiy and B. Miller]
– Possible applications:
• LBMHD (plasma physics), PARATEC (material science), CACTUS
(astrophysics), and GTC (magnetic fusion), which are able to fully utilize the
performance of machines comparable to the Earth Simulator and Cray X1
[Oliker, et al.]
• Sweep3D (using for daemon control investigation)
• SPECjAppServer2004 (Websphere, DB2)
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Dynamic Adaptability in Support of Extreme Scale
University of Wisconsin-Madison
Near-term Research Directions – 2
• Study of applications to determine profitability of
–
–
–
–
Dynamically adapting page size [Cascaval, et al.]
Daemon control (started) [D. Bailey and Hoisie, et al.]
Virtual memory management (just starting)
Dynamically adapting time quantum to control, e.g.,
process cache contention
• k-factor analysis to develop mathematical
models to guide I/O parameter set selection
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University of Wisconsin-Madison
References – 1
• Bailey, D., Private Communications, 2005.
• Borril, J., J. Carter, L. Oliker, D. Skinner, and R. Biswas, “Integrated
Performance Monitoring of a Cosmology Application on Leading
HEC Platforms,” Proceedings of the 2005 International Conference
on Parallel Processing (ICPP-05), June 2005.
• Cascaval, C., E. Duesterwald, P. Sweeney, and R. Wisniewski,
“Multiple Page Size Modeling and Optimization,” Proceedings of the
Fourteenth International Conference on Parallel Architectures and
Compilation Techniques (PACT-2005), September 2005.
• http://www.sourceforge.net/projects/ffsb
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University of Wisconsin-Madison
References – 2
• Hoisie, A., D. Kerbyson, S. Pakin, F. Petrini, H. Wasserman, and J.
Fernandez-Peinador, “Identifying and Eliminating the Performance
Variability on the ASCI Q Machine,” Technical Report LA-UR-030138, Performance and Architecture Lab, Los Alamos National
Laboratory, January 2003.
• Mirgorodskiy, A., and B. Miller, “Autonomous Analysis of Interactive
Systems with Self-propelled Instrumentation,” Proceedings of the
Multimedia Computing and Networking Conference, 2004.
• Moilanen, J., “Genetic Algorithms in the Kernel,”
http://kernel.jakem.net/, 2005.
• Oliker, L., A. Canning, J. Carter, J. Shalf, and S. Ethier, S. “Scientific
Computations on Modern Parallel Vector Systems,” Proceedings of
the 2004 ACM/IEEE Conference on Supercomputing, November
2004.
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University of Wisconsin-Madison
References – 3
• Pratt, S., and D. Heger, IBM-Austin, “Workload Dependent
Performance Evaluation of Linux 2.6 I/O schedulers,” Linux
Symposium, 2, July 2004, pp. 425-448.
• Tamches, A., and B. Miller, “Dynamic Kernel I-Cache Optimization,”
Proceedings of the Workshop on Binary Translation, September
2001.
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