GVR

GVR
Team Members	U. of Chicago, ANL, HP Labs
PI	Andrew A. Chien (U. of Chicago)
Co-PIs	Pavan Balaji (ANL)
Website	team website
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Global View for Resilience or GVR

Team Members

• University of Chicago: Andrew A. Chien (PI), Hajime Fujita, Zachary Rubenstein, Guoming Lu
• Argonne National Laboratory (ANL): Pavan Balaji (co-PI), James Dinan, Pete Beckman, Kamil Iskra
• HP Labs: Kamil Iskra

Application Partnerships

• Advanced Nuclear Reactor Simulation (Andrew Siegel, CESAR)
• Computational Chemistry (Jeff Hammond, ALCF)
• Rich Computational Frameworks (Mike Heroux, Sandia)
• ... and more!...

Resilience Challenges

• Can we achieve a smooth transition to system resilience? (a la Flash memory, Internet)
• What’s an application to do?

Resilience Co-design

Co‑design without co‑dependence

• Software: Information and Algorithms to enhance resilience (REQ: Portable, flexible)
• Runtime, OS, and Architecture Mechanisms to enhance resilience (REQ: leverage beyond HPC, cheap)

Challenges

• Enable an application to incorporate resilience incrementally, expressing resilience proportionally to the application need
• “Outside in”, as needed, incremental, ...

GVR Approach 1

• Application-System Partnership
  • Expose and exploit algorithm and application domain knowledge
  • Enable “End to end” resilience model

• Foundation in Data-oriented resilience
  • Internet services, map-reduce, internet, ...
  • Achieve with high performance and massive parallelism...
  • Global view data Foundation (PGAS..., GA, SWARM, ParalleX, CnC, ...)

Data-oriented Resilience

• Parallel applications and global-view data
• Natural parallel structure version-to-version
  • Example: shock hydro simulation at t=10ms to 100ms
  • Example: iterative solver at iteration 1 to 20
  • Example: monte carlo at 10M to 20M points

• Temporal redundancy enables rollback and resume
  • User-controlled, convenient

Resilience Partnership

• Proportional Resilience
  • Application specifies “Resilience priorities”
  • Mapped into data-redundancy in space
  • Mapped into redundancy in time (multi-version)
  • Complements computation/task redundancy efforts

• Deep error detection: invariants, assertions, checks ... and recovery

• Applications add further checks based on algorithm and domain semantics
  • Application add flexible, adaptive recovery mechanisms (and exploit multi-version)

• “End-to-end” resilience

GVR Approach 2

• x-layer approach for efficient execution (and better resilience)
  • Spatial redundancy – coding at multiple levels, system level checking
  • Temporal redundancy - Multi-version memory, integrated memory and NVRAM management

• Push checks to most efficient level (find early, contain, reduce overhead)
• Recover based on semantics from any level (repair more, larger feasible computation, reduce overhead)
• Efficient implementation support in runtime, OS, architecture ... increase efficiency and containment

Multi-version Memory

• Common parallel paradigm, basis for programmer engagement
• Frames invariant checks, more complex checks based on high-level semantics
• Frames sophisticated recovery

Research Challenges

• Understand application resilience needs and opportunities for proportional resilience and deep error detection/end-to-end resilience
• Explore multi-version memory as opportunity for framing richer resilience and parallelism
• Design API that embodies these ideas and gentle slope incremental application effort
• Create efficient x-layer implementations - many questions
• Explore architecture opportunities to increase resilience and reduce overhead

Global‑view Data Program

GVR Resilience Program

Global View & Consistent Snapshots

• How to safely, efficiently identify consistent snapshots?
  • Application control: Global Synch; Array-level synch; explicit snapshot
  • Application flagged (optional)
  • Implicit (runtime decides)
• Snapshots = natural points to express and implement assertions, checks, recovery

Implementing Multi-version

• How to implement multi-version efficiently?
  • Time, Space, Label => representation, protocol
• Which to take?
  • Versions are logical, snapshots require resources
• Intelligent storage:
  • Representation, compression, architecture support
  • Older versions recede into storage [SILT]

Intelligent Memory and Storage

• How to exploit intelligence at memory and storage? (at controller)
• Intelligent stacked DRAM and storage-class Memory [HMC,PIM]
• Fine-grained state tracking; compression, intelligent, copying, etc.
• Efficient version capture; differenced checkpoints (Plank95, Svard11)

Opportunities

• Multi-version and increased concurrency
• Multi-version and debugging
• Architecture support and fine-grained synchronization, application checks, compressed memory, etc.
• ...more?

Expected Outcomes

• Use cases – Application skeleton design and classifications which form foundation of the design
• Design of GVR API for flexible resilience and multi-version global data
• Research prototype software developed as a library; target for programmers, compiler backends
• Experiments with mini-apps and application partners (w/ co-design postdocs)
• Assessment of architecture support opportunities and quantitative benefits

GVR X-Stack Synergies

• Direct Application Programming Interface
• Co-existence, even target with other Runtimes
• Rich Solver Library Building Block
• Programming System Target