1. Software Enablement for Multicore Architectures
David Bernstein Bilha Mendelson

2. Conventional Bulk CMOS SOI (silicon-on-insulator)
Technology Scaling – We’ve Hit The Wall
0.2
0.4
0.6
0.8 1 2 4 6 8 10 20
1988
1992
1996
2000
2004
2008
2012
Conventional Bulk CMOS
SOI (silicon-on-insulator)
High mobility
Double-Gate
Year
Relative Device Performance ?
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3. Has This Ever Happened Before?
140
Bipolar
CMOS
IBM RY5
IBM GP
IBM RY6
Apache
Pulsar
Merced
IBM RY7
IBM RY4
Pentium II(DSIP)
Pentium 4
120
IBM ES9000 ?
100 80
Fujitsu VP2000
Watts / cm2
IBM 3090S 60
NTT
Fujitsu M-780 40
IBM 3090
Start of
CDC Cyber 205 20
Water Cooling
IBM 4381
IBM 3081
Fujitsu M380
IBM 370
IBM 3033
IBM 360
Vacuum
1950
1960
1970
1980
1990
2000
2010
Source: Bernie Meyerson, IBM
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4. Industry trends Intel Quad-Core Sun’s 8-Core Chips: T1 - Niagra
Cell Broadband Engine
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5. Hierarchy of Modular Building Blocks
Systems will increasingly need to implement a hybrid execution model
New programming systems need to reduce the need for programmer awareness of the topology on which their program executes
Grid/Cluster
High Speed Network
Hierarchical SMP servers with non-uniform memory access characteristics
Rack
High Speed Network
Hierarchical SMP servers with NUMA characteristics
Board
SMP Interconnect
Homogenous SMP on Board
2 – 128 HW contexts on board
Main Processor(s) with Accelerator(s)
Master-Slace relationship between entities
Memory
Memory
Chip
Homogenous SMP on chip
2-32 HW contexts on chip
Various forms of resource sharing
Heterogenous collection of processors on chip
Heterogenity at data and control flow level
Cache
I/O Attach
Interconnect Fabric
Mem Ctrl
Core
Core
The next gen programming system must support programming simplicity while leveraging the performance of the underlying HW topology.
Core
Core
Core will support multiple HW threads sharing a single cache exhibiting SMP characteristics.
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6. Architecture trends
Several processor cores on a chip and specialized computing engines
XML processing, cryptography, graphics
Questions:
how to interconnect large number of processor cores
how to provide sufficient memory bandwidth
how to structure the multilevel caching subsystem
how to balance the general purpose computing resources with specialized processing engines and all the supporting memory, caching and interconnect structure, given a constant power budget
Software development processes
how to program for multicore architectures
how to test and evaluate the performance of multithreaded applications
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7. Programming multiprocessor systems
Two main directions:
explicit manual programming
exploit the combination of compiler optimization, build tool chains, and run-time subsystems
In HPC and embedded communities:
emphasis was more on explicit manual programming and special resources by expert programmers
resulted in numerous home-grown language directives and extensions, internal tools, obscure run-time systems
hardly portable to new generations of hardware
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8. Programming languages
Very few new languages were invented in the last 2 decades
Java - virtual machine, interpreter, JIT, garbage collection, set of libraries, etc.
Can multicore spur development of new language/environment for parallelism?
map-reduce, cilk, UPC, X10, and STAPL
programmers can provide additional information related to parallelism
Multicore provide multiple types of parallelism
thread-level parallelism (TLP) – coarse-grain
OpenMP - standard for shared-memory models
MPI - standard for distributed-memory models
pthreads, java threads - explicitly use
automatic parallelization optimizations
Most of the original auto-parallelizing compilers focused on FORTRAN
data-level parallelism (DLP) – fine-grain
auto-vectorization, auto-simdification
What about asymmetric multicore architectures (like Cell processor)?
is it possible to have a single source compilation for multiple ISAs? - initial attempts…
how OpenMP can be used for programs - streaming
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9. Performance Analysis Tools
Profile based tools – data aggregation
FDPR-Pro, Code Analyzer, Diablo
Performance evaluation is heavily influenced by thread interaction
stales, locks, races, memory thrashing, pollute hardware counters
trace-based analysis and visualization
introduces timeline views and data to deal with communication issues
lack of scalability:
tend to grow fast, making it difficult to manipulate and visualize
In HPC context: selecting arbitrary subset of cores/threads and arbitrary time intervals
tracing might disturbs program's behavior
HPCToolkit, TAU, Paraver, VTune, Code Analyzer, PDT, Trace Analyzer
Lack of determinism
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10. Performance tools for multi-core: Cell
Visual Performance Analyzer 5.0
Cell SDK 3.0
Profile Analyzer
Code Analyzer
Pipeline Analyzer
Trace Analyzer
PDT
Lock Analyzer
Infrastructure for collecting profiles on several systems
Infrastructure for using databases for large data sets
Set of interconnected views
Cell support
Infrastructure for collecting traces on SDK 3.0 libraries
Analysis of lock usage
Input for Trace Analyzer
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11. Debugging and testing tools
Concurrent problems constitute about 10% of the bugs
Bugs like crashes (races) or freeze (deadlocks) stay in the application reducing the up-time
Testing is done at load testing - very late in the process
We have been working on a tool supported methodology
try to find the concurrency issues as early as possible:
teach how to write concurrent code
concurrent bug patterns
explain the concurrent programming constructs
teach general concurrency design patterns
reviews - developed a specialized review technique for concurrent code
teach how to do unit testing - developed synchronization coverage
ConTest - a tool supported method for measuring contention
Make the tests that are likely to exhibit bugs - changing the internal timing
Tools for pinpointing locations of bugs
if we have a test that we can cause the application to fail some of the time
healing bugs so that the impact will not be seen
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12. Software trends Software enablement system for multicores
Various directions for providing solutions
Active area of research
only some early results in the academic and industrial worlds in terms of established standards and technology
much more will evolve in the years to come
Need:
programming models and compiler support for multicores
performance evaluation tools
testing and debugging tools
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13. Thank You
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