the 3rd international conference on emerging ubiquitous systems and pervasive networks amman,...
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The 3rd International Conference on Emerging Ubiquitous Systems and
Pervasive Networkswww.iasks.org/conferences/EUSPN2011
Amman, JordanOctober 10-13, 2011
Challenges to High Productivity Computing Systems and
Networks
Mohammad MalkawiDean of Engineering,
Jadara University
Outline
High Productivity Computing Systems (HPCS) - The Big Picture
The Challenges IBM PERCS Cray Cascade SUN Hero Program Cloud Computing
HPCS: The Big Picture
Manufacture and deliver a peta-flop class computer
Complex architecture High performance Easier to program Easier to use
HPCS Goals
Productivity Reduce code development time
Processing power Floating point & integer arithmetic
Memory Large size, high bandwidth & low
latency Interconnection
Large bisection bandwidth
HPCS Challenges
High Effective Bandwidth High bandwidth/low latency memory systems
Balanced System Architecture Processors, memory, interconnects,
programming environments Robustness
Hardware and software reliability Compute through failure Intrusion identification and resistance
techniques.
HPCS Challenges
Performance Measurement and Prediction
New class of metrics and benchmarks to measure and predict performance of system architecture and applications software
Scalability Adapt and optimize to changing workload and
user requirements; e.g., multiple programming models, selectable machine abstractions, and configurable software/hardware architectures
Productivity Challenges
Quantify productivity for code development and production
Identify characteristics of ➔ Application codes➔ Workflow➔ Bottlenecks and obstacles➔ Lessons learned so that decisions by the
productivity team and the vendors are based on real data rather than anecdotal data
Did Not Learn the Lessons
Defect Arrival Rate
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Figure 2: Defect Arrival Rate for R8, R9 and R10
Productivity Dilemma - 1
Diminishing productivity is alarming Coding Debugging Optimizing Modifying Over-provisioning hardware Running high-end applications
Productivity Dilemma - 2
Not long ago, a computational scientist could personally write, debug and optimize code to run on a leadership class high performance computing system without the help of others.
Today, the programming for a cluster of machines is significantly more difficult than traditional programming, and the scale of the machines and problems has increased more than 1,000 times.
Productivity Dilemma - 3
Owning and running high-end computational facilities for nuclear research, seismic modeling, gene sequencing or business intelligence, takes sizeable investment in terms of staffing, procurement and operations.
Applications achieve 5 to 10 percent of the theoretical peak performance of the system.
Applications must be restarted from scratch every time a hardware or software failure interrupts the job.
HPCS Trends: Productivity Crisis
High Productivity Computing
Scaling the Program Without Scaling the
Programmer
Bandwidth enables productivity and allows for simpler programming environments and systems with
greater fault tolerance
Language Challenges
MPI is a fairly low-level language Reliable, predictable and works. Extension of Fortran, C and C++
New languages with higher level of abstraction
Improve legacy applications Scale to Petascale levels
SUN – Fortress➔ IBM - X10➔ Cray – Chapel➔ Open MP
Global View Programming Model
Global View programs present a single, global view of the program's data structures,
Begin with a single main thread. Parallel execution then spreads out
dynamically as work becomes available.
Unprecedented Performance Leap
Performance targets require aggressive improvements in system parameters traditionally ignored by the "Linpack" benchmark.
Improve system performance under the most demanding benchmarks (GUPS)
Determine whether general applications will be written or modified to benefit from these features.
Trade-Offs
Portability versus innovations Abstractions vs. difficulty of
programming and performance overhead
Shared memory versus message passing
Cost of Petascale Computing
Require petabytes of memory Order of 106 processors Hundreds of petabytes of disk storage for
capacity and bandwidth. Power consumption and cost for DRAM
and disks (Tens of Mega Watts) Operational cost
The DARPA HPCS Program
First major program to devote effort to make high end computers more user-friendly
Mask the difficulty of developing and running codes on HPCS
Mask the challenge of getting good performance for a general code
Fast, large, and low latency RAM Fast processing Quantitative measure of productivity
IBM HPCS EXAMPLE
IBM HPCS Program – PERC 2011 Productive, Easy-to-use, Reliable Computer Rich programming environment
Develop new applications and maintain existing ones. Support existing programming models and languages Scalability to the peta-level
Automate performance tuning tasks Rich graphical interfaces Automate monitoring and recovery tasks Fewer system administrators to handle
larger systems more effectively
IBM Blue Gene – HPCS Base
IBM Approach - Hardware
Innovative processor chip design & leverage the POWER processor server line.
Lower Soft Error Rates (SER) Reduce the latency of memory accesses by
placing the processors close to large memory arrays.
Multiple chip configuration to suit different workloads.
IBM Approach - Software
Large set of tools integrated into a modern, user-friendly programming environment.
Support both legacy programming models and languages (MPI, OpenMP, C, C++, Fortran, etc.),
Support emerging ones (PGAS) Design new experimental programming
language, called X10.
X10 Features Designed for parallel processing from the
ground up. Falls under the Partitioned Global Address
Space (PGAS) category Balance between a high-level abstraction
and exposing the topology of the system Asynchronous interactions among the
parallel threads Avoid the blocking synchronization style
CRAY HPCS EXAMPLE
Multiple Processing Technologies
In high performance computing: one size does not fit all
Heterogeneous computing using custom processing technologies.
Performance achieved via deeper pipelining and more complex microarchitectures
Introduction of multi-core processors: Further stresses processor-memory balance issues Drives up the number of processors required to solve
large problems
Specialized Computing Technologies
Vector processing and field programmable gate arrays (FPGAs)
➔ Ability to extract more performance out of the transistors on a chip with less control overhead.
➔ Allow higher processor performance, with lower power
➔ Reduce the number of processors required to solve a given problem
➔ Vector processors tolerate memory latency extremely well
Specialized Computing Technologies
Multithreading improve latency tolerance Cascade design will combine
multiple computing technologies Pure scalar nodes, based on Opteron
microprocessors Nodes providing vector, massively
multithreaded, and FPGA-based acceleration.
Nodes that can adapt their mode of operation to the application.
Cray: The Cascade Approach Scalable, high-bandwidth system Globally addressable memory Heterogeneous processing technologies Fast serial execution Massive multithreading Vector processing and FPGA-based
application acceleration. Adaptive supercomputing:
➔ The system adapts to the application rather than requiring the programmer to adapt the application to the system.
Cascade Approach
Use Cray T3ETM massively parallel system Use best-of-class microprocessor Processors directly access global memory
with very low overhead and at very high data rates.
Hierarchical address translation allows the processors to access very large data sets without suffering from TLB faults
AMD's Opteron will be the base processor for Cascade
Cray – Adaptive Supercomputing
The system adapts to the application The user logs into a single system, and sees
one global file system. The compiler analyzes the code to
determine which processing technology best fits the code
The scheduling software automatically deploys the code on the appropriate nodes.
Balanced Hardware Design
A balanced hardware design Complements processor flops with memory,
network and I/O bandwidth Scalable performance Improving programmability and breadth
of applicability. Balanced systems also require fewer
processors to scale to a given level of performance, reducing failure rates and administrative overhead.
Cray- System Bandwidth Challenge
The Cascade program is attacking this problem on two fronts
Signalling technology and Network design.
Provide truly massive global bandwidth at an affordable cost.
A key part of the design is a common, globally addressable memory across the whole machine.
Efficient, low-overhead communication.
Cray- System Bandwidth Challenge
Accessing remote data is as simple as issuing a load or store instruction, rather than calling a library function to pass messages between processors.
Allows many outstanding references to be overlapped with each other and with ongoing computation.
Cray Programming Model Support MPI for legacy purposes Unified Parallel C (UPC) and Coarray
Fortran (CAF) simpler and easier to write than MPI
Reference memory on remote nodes as easily as referencing memory on the local node
Data sharing is much more natural Communication overhead is much lower.
The Chapel – Cray HPCS Language
Support for graphs, hash tables, sparse arrays, and iterators.
Ability to separate the specification of an algorithm from structural details of the computation including
Data layouts Work decomposition and communication. Simplifies the creation of the basic algorithms Allows these structural components to be
gradually tuned over time.
Cray's Programming Tools
Reduce the complexity of working on highly scalable applications.
The Cascade debugger solution will Focus on data rather than control Support application porting Allow scaling commensurate with the
application Integrated user environment (IDE)
Cascade Performance Analysis Tools
Hardware performance counters Software introspection techniques. Present the user with insight, rather than
statistics. Act as a parallel programming expert Provide high-level feedback on program
behaviour Provide suggestions for program
modifications to remove key bottlenecks or otherwise improve performance.
SUN HPCS EXAMPLE
Evolution of HPCS at SUN
Grid: Loosely coupled heterogeneous resources Multiple administrative domains Wide area network
Clusters Tightly coupled high performance systems Message passing – MPI
Ultrascale Distributed scalable systems High productivity shared memory systems High bandwidth, global address space, unified
administration tools
SUN Approach – The Hero System
Rich bandwidth Low latencies Very high levels of fault tolerance Highly integrated toolset to scale the
program and not the programmers Multithreading technologies ( > 100
concurrent threads)
SUN Approach – The Hero System
Globally addressable memory System level and application
checkpointing Hardware and software telemetry for
dramatically improved fault tolerance. The system appears more like a flat
memory system Focus on solving the problem at hand
rather than making elaborate efforts to distribute data in a robust manner.
Definition: Bisection Bandwidth
Example is an all-to-all interconnect between 8 cabinets
There are 28 total connections, of which 16 cross the bisection (orange) and 12 do not (blue)
High bandwidth optical connections are key to meeting HPCS peta-scale bisection bandwidth target
Split a system into equal halves such that there is the minimum number of connections across the
split- the bandwidth across the split is the bisection bandwidth
A standard metric for system’s ability to globally move data
System Bandwidth Over TimeA giant leap in productivity expected
High Bandwidth Required by HPCSRadical Changes From Today’s Architecture Necessary
Motivation for Higher Bandwidth
Growing BW demand in HPCS
➔ Multicore CPUs: Aggregation of multiple cores is unstoppable and copper interconnects are stressed at very large scale
➔ Silicon Photonics is the solution since it brings a potential of unlimited BW on the best medium allowing for large aggregation of multicore CPUs
Growing BW demand in HPCS
Clusters are growing in number of nodes and in performance/node
Interconnects are the limiting factor in BW, latency, distance
Protocols reduce latency & copper increases latency.
Silicon Photonics brings high BW and low latency
Growing BW demand in HPCS
Storage I/O BW increasing exponentially due to the faster data/rate and the parallelism caused by striping technologies
WDM will eventually allow 10Tb of data to be transmitted down a single piece of fiber
Silicon Photonics is at the beginning of its life cycle with headroom for explosive BW growth without any increase in latency or reduction in reach
Proximity + CMOS Photonics
Proximity Communication -2
Proximity Communication -3
Proximity Communication Capacitive coupling enables high-speed
data communication between neighboring chips without the need for wires of any kind
➔ Allows for the alignment of metal plates on one chip with metal plates on a neighboring chip and the transfer of data between them
➔ reduced power ➔ improves cross-section bandwidth
and➔ communication power
Proximity Communication - SUN➔ 3.6 x 4.1 mm test chip➔ 0.35 um technology➔ 50 um bit pitch➔ 1.35 Gbps/channel for 16
simultaneous channels➔ < 10^-12 BER @ 1Gbps➔ 3.6 mW/channel static
power➔ 3.9 pJ/bit dynamic power
Proximity Communication -4
Proximity Communication -5
Low Cost, Low Power Optics
DWDM CMOS Photonics
CMOS Photonics Module
SUN Programming ModelSimpler Code with High Bandwidth Shared MemoryNAS Parallel Benchmark CG (Conjugate Gradient) Lines of Code
SUN Fortress Language
➔ Catch stupid mistakes➔ Extensive libraries➔ Platform indpendence➔ Security model➔ Type safety➔ Multithreading➔ Dynamic compilation
To Do For Fortran What JavaTM Did For C
Object-Based “Smart” Storage
With Object Storage File Systems For Massive Scalability and Extreme Performance
Ultra-scale Computing in 2010 Simpler development environments will
make HPC more accessible to a diverse range of users
Lone researchers and small teams will once again be able to harness the computational power of leadership class systems
Many gaps regarding commercial and scientific computing will narrow
Cloud Computing
Service computing The net is the computer More than 100 vendors Growing fast Programming environment
BACKUP SLIDES
HPCS TechnologiesSome Publicly Announced Projects
IBM HPCS - PERCS
➔ Open source operating systems and hypervisors will provide HPC-oriented
➔ Virtualization➔ Security➔ Resource management ➔ Affinity control ➔ Resource limits➔ Checkpoint-restart and reliability features
that will improve the robustness and availability of the system.
MPI Paradigm➔ Writing applications in MPI requires breaking up all the data and
computation into a large number of discrete pieces➔ and then using library code to explicitly bundle up data and pass it
between processors in messages whenever processors need to share data.
➔ It's a cumbersome affair that distracts scientists from their primary focus.
➔ Once an application is written, it's generally a time-consuming process to debug and tune it.
➔ Traditional debugging models just don't scale well to thousands or tens of thousands of processors (try opening up 10,000 debugger windows, one for each thread!).
➔ Trying to figure out why your application isn't getting the performance you think it should is also exceedingly difficult at large scales.
➔ Traditional profiling and even sophisticated statistics-gathering may be insufficient to ascertain why the performance is lagging, much less how to change the code to improve it.
Productivity Challenges
The time spent trying to structure an application to fit the attributes of the target machine.
If the machine is a cluster with limited interconnect bandwidth
➔ the programmer must carefully minimize communication
➔ make sure that any sparse data to be communicated is first bundled together into larger messages to reduce communication overheads.
Productivity Challenges If the machine uses conventional
microprocessors Care must be taken to maximize cache re-
use Eliminate global memory references,
which tend to stall the processor. If the machine looks like a hammer
You'd better make all your codes look like nails!
This can lead to "unnatural" algorithms and data structures, which significantly reduces programmer productivity