Transcript ATPAC/ICT Team - Louisiana Tech University

Introduction: HPC goes mainstream
Chokchai Box Leangsuksun
Associate Professor, Computer Science
Louisiana Tech University
[email protected]
1
Outline
• Why HPC is critical technology ?
• Conclusion
13 April 2015
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Why HPC?
• High Performance Computing – Parallel , Supercomputing
– Enabled by multiple high speed CPUs, networking, software etc –
fastest possible solution
– Technologies that help solving non-trivial tasks including scientific,
engineering, medical, business entertainment and etc.
• Time to insights, Time to discovery, Times to markets
• BTW, HPC is not GRID!!!.
13 April 2015
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HPC Applications and Major Industries
• Finite Element Modeling
– Auto/Aero
• Fluid Dynamics
– Auto/Aero, Consumer Packaged Goods Mfgs,
Process Mfg, Disaster Preparedness (tsunami)
• Imaging
– Seismic & Medical
• Finance
– Banks, Brokerage Houses (Regression Analysis,
Risk, Options Pricing, What if, …)
• Molecular Modeling
– Biotech and Pharmaceuticals
Complex Problems, Large Datasets, Long Runs
This slide is from Intel presentation “Technologies for Delivering Peak Performance on HPC and Grid Applications”
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Life Science Problem – an example
of Protein Folding
• Take a computing year (in serial mode) to do molecular dynamics
simulation for a protein folding problem
•Excerpted from IBM David Klepacki’s The future of HPC
13 April 2015
•Petaflop = a thousand trillion floating point operations per second
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Disaster Preparedness - example
• Project LEAD
– Severe Weather prediction
(Tornado) – OU leads.
• HPC & Dynamically
adaptation to weather
forecast
• Professor Seidel’s LSU CCT
– Hurricane Route Prediction
– Emergency Preparedness
– Show Movie – HPC-enabled
Simulation
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Did you know that Playstation 3 is a
HPC/Supercomputer?
•
•
9 cores/CPUs in one chip.
Future gaming software is no longer graphic or multimedia only
•
This diagram is from an article from IBM Cell processor & compiler challenge
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No Free Lunch (mainstream CPUs)
• CPU speed – plateaus 3-4
Ghz
• More cores in a single
chip
– Dual core is now
– Multicore is imminent
3-4 Ghz cap
• Traditional Applications
won’t get a free rides
• Conversion to parallel
computing (HPC, MT)
This diagram is from “no free lunch article in DDJ
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Cancer Gene-mining
•
•
Unsuccessful on a uni-processor
Our approach
– Novel parallel gene-mining
algorithms
– Input from microarray
– Retain accuracy
– Significantly speed up
(superlinear)
•
IBM P5 supercomputer (128 node
PPC).
Time to run the algorithm, keeping number of nodes fixed
M es othelioma
Time taken(in secs)
1200
Breas t
80
60
Renal
1000
Bladder
100
L eukemia
40
800
20
P ros tate
600
0
L ung
400
P anc reas
200
0
C olorec tal
O vary
13
39
65
91
L ymphoma
M elanoma
Number of processors
O vaM arker bas ed Selec tion
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G eneSetM ine bas ed Selec tion
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Significant indicators – why HPC
now?
•
•
•
“I propose to double the
commitment to the
No Free lunch in CPU speed up (Intelfederal
or AMD)
mostatcritical
– In past 1-2 years, CPU speed was flatten
3+ Ghzbasic research
programschips
in the physical
– More CPUs in one chip – Dual core, multi-core
– Traditional software won’t take advantage
of these
sciences
overnew
theprocessors
next 10
– Personal/Desktop Supercomputing. years. This funding will support
Many real problems are highly computational
the work intensive.
of America's most
– NSA uses supercomputing to do datacreative
mining minds as they explore
– DOE – fusion, plasma, energy relatedpromising
(including areas
weaponry).
such as
– Help solving many other important areas
(nanotech, life science etc.)
nanotechnology,
Giants recently sneeze out HPC
supercomputing, and
– Bush’s state of union speech – 3 mainalternative
S&T focusenergy
of which
Supercomputing is one
sources.”
of them
– Bill Gates’ keynote speech at SC05 – MS goes after HPC
•
•
•
Gorge W. Bush, 2005
Google search engine - 100,000 nodes
Playstation 3 is a personal supercomputing platform
Hollywood (Entertainment) is HPC-bound (Pixar – more than 3000 CPUs to
render animation)
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HPC preparedness
• Build work forces that understand HPC paradigm
& its applications
– HPC/Grid Curriculum in IT/CS/CE/ICT
– Offer HPC-enabling tracks to other disciplinary
(engineering, life science, physic, computational chem,
business etc..)
– Training business community (e.g. HPC for enterprise ;
Fluent certification, HA SLA certification)
– Bring awareness to public
• .
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Introduction to Parallel computing
• Need more computing power
– Improve the operating speed of processors & other
components
• constrained by the speed of light, thermodynamic laws, &
the high financial costs for processor fabrication
– Connect multiple processors together & coordinate their
computational efforts
• parallel computers
• allow the sharing of a computational task among multiple
processors
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How to Run Applications Faster
?
• There are 3 ways to improve performance:
– Work Harder
– Work Smarter
– Get Help
• Computer Analogy
– Using faster hardware
– Optimized algorithms and techniques used to solve
computational tasks
– Multiple computers to solve a particular task
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Era of Computing
– Rapid technical advances
• the recent advances in VLSI technology
• software technology
– OS, PL, development methodologies, & tools
• grand challenge applications have become the main
driving force
– Parallel computing
• one of the best ways to overcome the speed bottleneck
of a single processor
• good price/performance ratio of a small cluster-based
parallel computer
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HPC Level-setting
Definitions
• High performance computing is:
– Computing that demands more than a single highmarket-volume workstation or server can deliver
• HPC is based on concurrency:
– Concurrency: computing in which multiple tasks are
active at the same time
• Parallel computing occurs when you use
concurrency to:
– Solve bigger problems
– Solve a fixed-size problem in less time
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HPC Level-setting
Hardware for Parallel Computing
Parallel Computers
Single Instruction Multiple
Data (SIMD)§
Shared Address Space
Symmetric
Multiprocessor
(SMP)
Non-uniform
Memory
Architecture
(NUMA)
Multiple Instruction
Multiple Data (MIMD)
Disjoint Address Space
Massively
Parallel
Processor
(MPP)
Commodit
y Cluster
Distributed
Computing
§SIMD has failed as a way to organize large-scale computers with multiple processors. It has succeeded, however,
as a mechanism to increase instruction-level parallelism in modern microprocessors (in Intel® MMX™ technology).
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Scalable Parallel Computer
Architectures
• MPP
– A large parallel processing system with a shared-nothing
architecture
– Consist of several hundred nodes with a high-speed
interconnection network/switch
– Each node consists of a main memory & one or more processors
• Runs a separate copy of the OS
• SMP
–
–
–
–
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2-64 processors today
Shared-everything architecture
All processors share all the global resources available
Single copy of the OS runs on these systems
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Scalable Parallel Computer
Architectures
• CC-NUMA
– a scalable multiprocessor system having a cache-coherent nonuniform
memory access architecture
– every processor has a global view of all of the memory
• Distributed systems
– considered conventional networks of independent computers
– have multiple system images as each node runs its own OS
– the individual machines could be combinations of MPPs, SMPs,
clusters, & individual computers
• Clusters
– a collection of workstations of PCs that are interconnected by a highspeed network
– work as an integrated collection of resources
– have a single system image spanning all its nodes
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Cluster Computer and its Architecture
• A cluster is a type of parallel or distributed processing system,
which consists of a collection of interconnected stand-alone
computers cooperatively working together as a single, integrated
computing resource
• A node
–
–
–
–
–
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a single or multiprocessor system with memory, I/O facilities, & OS
generally 2 or more computers (nodes) connected together
in a single cabinet, or physically separated & connected via a LAN
appear as a single system to users and applications
provide a cost-effective way to gain features and benefits
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Cluster Computer Architecture
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Beowulf
Head Node
•Login
•Compile
•Submit job
Compute nodes
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•Run tasks
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Prominent Components of
Cluster Computers (I)
• Multiple High Performance
Computers
– PCs
– Workstations
– SMPs (CLUMPS)
– Distributed HPC Systems leading to
Metacomputing
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Prominent Components of
Cluster Computers (II)
• State of the art Operating Systems
–
–
–
–
–
–
–
Linux
(Beowulf)
Microsoft NT (Illinois HPVM)
SUN Solaris (Berkeley NOW)
IBM AIX(IBM SP2)
HP UX
(Illinois - PANDA)
Mach (Microkernel based OS) (CMU)
Cluster Operating Systems (Solaris MC, SCO Unixware,
MOSIX (academic project)
– OS gluing layers
(Berkeley Glunix)
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Prominent Components of
Cluster Computers (III)
• High Performance Networks/Switches
– Ethernet (10Mbps), Fast Ethernet (100Mbps),
– InfiniteBand (1-8 Gbps)
– Gigabit Ethernet (1Gbps)
– SCI (Dolphin - MPI- 12micro-sec latency)
– ATM
– Myrinet (1.2Gbps)
– Digital Memory Channel
– FDDI
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Prominent Components of
Cluster Computers (IV)
• Network Interface Card
– Myrinet has NIC
– InfiniteBand (HBA)
– User-level access support
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Prominent Components of
Cluster Computers (VI)
• Cluster Middleware
– Single System Image (SSI)
– System Availability (SA) Infrastructure
• Hardware
– DEC Memory Channel, DSM (Alewife, DASH), SMP Techniques
• Operating System Kernel/Gluing Layers
–
Solaris MC, Unixware, GLUnix
• Applications and Subsystems
–
–
–
Applications (system management and electronic forms)
Runtime systems (software DSM, PFS etc.)
Resource management and scheduling software (RMS)
•
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CODINE, LSF, PBS, NQS, etc.
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Prominent Components of
Cluster Computers (VII)
• Parallel Programming Environments and Tools
– Threads (PCs, SMPs, NOW..)
• POSIX Threads
• Java Threads
– MPI
• Linux, NT, on many Supercomputers
– PVM
– Software DSMs (Shmem)
– Compilers
• C/C++/Java
• Parallel programming with C++ (MIT Press book)
– RAD (rapid application development tools)
• GUI based tools for PP modeling
– Debuggers
– Performance Analysis Tools
– Visualization Tools
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Prominent Components of
Cluster Computers (VIII)
• Applications
– Sequential
– Parallel / Distributed (Cluster-aware app.)
• Grand Challenging applications
–
–
–
–
–
Weather Forecasting
Quantum Chemistry
Molecular Biology Modeling
Engineering Analysis (CAD/CAM)
……………….
• PDBs, web servers,data-mining
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Key Operational Benefits of Clustering
•
•
•
•
High Performance
Expandability and Scalability
High Throughput
High Availability
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Divide and Conquer
• Says 1 CPU
– 1,000,000 elements
– Numerical processing for 1
element = .1 secs
– One computer will take
100,000 secs = 27.7 hrs
• Says 100 CPUs
– .27 hr ~ 16 mins
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Parallel Computing
• A big application is divided into Multiple tasks
• Total computation time
– Computing time
– Communication time
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Summary
• HPC helps accelerates Time to insights, time to
discovery and time to Market for challenging
problems
• Divide and Conquer
– Computing vs communication time
• Cluster computing is a predominant HPC system
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