Programming Models for Exascale Systems: What, When and...
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Programming Models for Exascale Systems: What, When and How? Kathy Yelick Associate Laboratory Director for Computing Sciences Lawrence Berkeley National Laboratory and Professor of Electrical Engineering and Computer Science UC Berkeley
Transcript of Programming Models for Exascale Systems: What, When and...
Programming Models for Exascale Systems: What, When and How?!
!Kathy Yelick!
Associate Laboratory Director for Computing Sciences!Lawrence Berkeley National Laboratory !
and !Professor of Electrical Engineering and Computer Science!
UC Berkeley
1. Lightweight cores will have all/most of the system performance – Need fine-‐grained parallelism; avoid unnecessary synchroniza8on – Cores not powerful enough for complex communica8on protocols ?
2. On-‐chip interconnect offers opportuni=es for performance – New models of communica8on may be essen8al
3. Hardware is heterogeneous: no single ISA – Portability and performance portability are challenging
4. New levels of memory hierarchy, possibly soIware-‐controlled – Locality and communica8on-‐avoidance paramount
5. Performance variability may increase – SoCware or hardware control clock speeds
6. Resilience will be paramount at scale – Failures grow with the number of components and connec8ons
Why is Change Necessary?
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Don’t Overstake Claims – Prove them!
Programing Models and Environments 3
The Trade-Off Is With Us
Programing Models and Environments 4
Locality Load Balance
• Target new applica=ons, not rewri=ng old ones – The mo8va8on is higher (all that code to be wriIen) – The barrier to entry is lower (no code to throw away)
• Target parts of applica=ons, not full rewrites – Support for incremental adop8on is key – Need to leverage exis8ng libraries and parts of exis8ng codes
• Target new programming paOerns – Irregular-‐in-‐space communica8on (random access to large data) – Irregular-‐in-‐8me (one-‐sided access to shared state) – Irregular workload (dynamic load balancing)
• Look for low hanging performance fruit – Do you rooflining homework – What’s the threshold? 15% -‐ no, 15x -‐ yes
Approach to Adoption
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• Target new applica=ons, not rewri=ng old ones – The mo8va8on is higher (all that code to be wriIen) – The barrier to entry is lower (no code to throw away)
• Target parts of applica=ons, not full rewrites – Support for incremental adop8on is key – Need to leverage exis8ng libraries and parts of exis8ng codes
• Target new programming paOerns – Irregular-‐in-‐space communica8on (random access to large data) – Irregular-‐in-‐8me (one-‐sided access to shared state) – Irregular workload (dynamic load balancing)
• Look for low hanging performance fruit – Do you rooflining homework – What’s the threshold? 15% -‐ no, 15x -‐ yes
Approach to Adoption
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Work with applica8ons people to understand
what they need!
Random Access to large state
Dynamic load
balancing
Fusion of observa8on
into simula8on
Unstruct-‐ured DAG algorithms
Application Challenges (Opportunities?)
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Infinite 15-‐20% Infinite 2x Speedup
Programming mode role: encourage a way of thinking
Lightweight mechanisms, flexible policies • Atomic remote memory opera=ons (compare&swap,…) • 1-‐way put + signal (2 addresses, data + increment) • SoIware-‐managed memory as an op#on • Fault detec=on and resilience job schedulers, etc. • Transparent performance informa=on
Hardware changes desired/required?
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Give an applica=on some op=miza=ons and you feed it for a
machine.
Teach a computer to generate op=mized code for an applica=on
and you feed it for a life=me
Thank you!
• What kind of programming models will be good for Exascale systems? Will it be evolu=onary or revolu=onary?
• Will MPI+X (together with PGAS and Task-‐based models) will be best suited for Exascale systems?
• Will newer programming models like HPC++, Legion, HPX, etc. gain more trac=on?
• How will the upcoming hardware trend (accelerators, coprocessors, high bandwidth memory, on-‐chip networking, etc.) have impact on the suitability of programming models?
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