Optimization ofCoupled Systems

on Emerging Architectures

Gerhard TheurichNRL/SAIC

ESMF Executive Board / Interagency Working Group MeetingJune 12, 2014

Applications are trending toward larger numbers of components:– Coupling with multiple time-scales

– Explicit, semi-implicit and, fully implicit schemes

– High resolution, adaptive, unstructured grids

– Hierarchical versus flat component architectures

– Ensembles: multi-instance, multi-model, concurrent versus sequential.

To make matters worse:

The growing complexity of coupled systems is compounded by the increasing levels of explicit parallelism on emerging computing architectures.

Coupling challenges

HPC hardware is a changing element 1980s – Vector machines

– SIMD type parallelism of serial code.

1990s – Parallel machines

– Coarse grain parallelism.

Early 2000s – Massively parallel machines (some parallel vector)

– Distributed memory.

– Distributed shared memory.

– MPI as the standard to implement coarse grain parallelism.

Early 2010s – Massively parallel machines with multi-core CPUs

– Hybrid coarse and fine grain parallelism: MPI+threads (e.g. OpenMP)

Today – Heterogeneous systems

– Heterogeneous HW: Multi-core CPUs + GPUs + MICsNot every node the same: different #CPU sockets, memory size & devices

– Heterogeneous SW: MPI+OpenMP + CUDA/PGI-ACC/Cray-ACC/OpenACC/Intel-MIC-Directives/Intel-MIC-Native/OpenCL

Host – multi-core CPUGPU

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O(10k)nodes

RAM

Host – multi-core CPUMIC

RAM

Host – multi-core CPU

RAM

GPU

GPU

GPU

cores

cores

cores MIC

Increasing course grain parallelism:Decomposition and distribution.

Increasing fine grain parallelism:Threading and SIMD on CPU cores, and device cores.

Modern supercomputer architecture

Earth system models on accelerators

Focus has been on specific models. Optimization of the computationally intensive

kernels to take advantage of the extra level of parallelism offered by accelerators.

Examples:

– WRF Single Moment 5-tracer (WSM5) on Nvidia GPU (Michalakes, et al.)

– NEMS/NMMB on Intel MIC (Michalakes)

Coupling challenges on modern architectures

Components are distributed across O(10k) nodes.

Field and grid data is stored and processed on different hardware throughout a run within a component and between components.

Efficient coupling requires data locality between the components to reduce the cost of data movements.

A limited number of each type of processing hardware (and memory) is available and must be shared between the components.

Efficient use of the available hardware requires some over-subscription, but suffers from too much.

ESMF and the NUOPC Layer offer key elements to address the coupling challenges

Support for a wide range of multi-model application architectures.

Data types that can represent a large range of structured and unstructured grids.

Data types that can represent data decompositions and their distribution across the underlying hardware.

Methods to move data efficiently between decompositions/distributions.

A well defined set of initialization sequences with multi-way negotiation between the components.

Grids and decomposition information can be transferred between components during initialization.

Host – multi-core CPUGPU

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Host – multi-core CPUGPU

Host – multi-core CPUGPU

Comp-A Comp-B Comp-C

Example of interleaved components

cores

cores

cores

ESMF/NUOPC accelerator projects ESMF team efforts are funded through ONR/Earth System Prediction

Capability

Target is a suite of coupled models for next generation Naval prediction

1 year ONR seed project: Optimized Infrastructure for the Earth System Prediction Capability includes prototype accelerator support (began May 2013)

3 year ONR project: An Integration and Evaluation Framework for ESPC Coupled Models includes delivery of capability for coupled systems

Specific projects we interact with under ONR include:

– Accelerated Prediction of the Polar Ice and Global Ocean (APPIGO)

– NPS-NRL-Rice-UIUC Collaboration on Navy Atmosphere-Ocean Coupled Models on Many-Core Computer Architectures

Our part is to look into accelerators with ESMF/NUOPC specifically for coupled systems.

Can components that use different programming models (OpenCL, OpenACC, Intel-MIC-Directives, …) run under the same single ESMF executable?

Do the different programming models provide enough control for a component to decide at run-time whether to use a specific accelerator device or not?

Is it possible to uniquely identify the available devices? Across programming models? Across the distributed parts of a component? Across components?

Initial questions and considerations

Jayesh Krishna (ANL) has prototyped ESMF components with OpenCL, OpenACC, and Intel-MIC-Directives.

Feature OpenCL OpenACC Intel-MIC-Directives

Can be combined in ESMF application

YES YES YES

Control offloading during runtime

YES YES NO?

Identify the available resources

YES (but) GPUs+MICs, within OpenCL

YES (but)GPUs, within OpenACC

YES (but)MICs, within MIC-Directives

Early prototyping

Offer access to device information through ESMF: enough to guide a driver component to do component placement (interleaved components).

Support data references for the most efficient exchange between sequential components that are placed on the same compute resources.

Prototype the inter-component negotiation of distributions by looking at the optimization problem of model grid distribution within the mediator component.

Explore the possibility of automated construction of interleaved component based on the discovered resources and hints provided by the components during the initialization negotiation.

Next steps

Thank you!

Project page on Earth System CoG:

https://earthsystemcog.org/projects/couplingtestbed/acceleratorplans