Exascale Bottlenecks

The Exa-MA scientific document identifies thirteen exascale bottlenecks. Exa-MA directly addresses the methodological and algorithmic bottlenecks B7-B11 and B13, while B1-B6 and B12 are addressed through transverse collaborations across NumPEx.

1. Hardware, System, and Data Bottlenecks

These bottlenecks are mainly handled through collaborations with other NumPEx projects, but they constrain the methods, algorithms, software, and demonstrators developed in Exa-MA.

ID Bottleneck Definition

ID	Bottleneck	Definition
`B1`	Energy efficiency	Develop energy-efficient technologies to keep exascale systems within the target power envelope.
`B2`	Interconnect technology	Improve vertical intra-node and horizontal inter-node data movement in terms of energy efficiency and performance.
`B3`	Memory technology	Integrate new memory technologies to improve capacity, bandwidth, resiliency, and energy efficiency.
`B4`	Scalable system software	Increase the scalability, power sensitivity, and resiliency of system software, including operating systems, runtimes, and monitoring.
`B5`	Programming systems	Develop programming paradigms that express fine-grained concurrency, locality, and resilience.
`B6`	Data management	Develop software that handles massive data volumes, including data analysis, compression, fault tolerance, and large-scale I/O.
`B12`	Pre/post processing	Scale visualization, in situ processing, and the preparation and analysis steps that surround large simulations.

B1

Energy efficiency

Develop energy-efficient technologies to keep exascale systems within the target power envelope.

B2

Interconnect technology

Improve vertical intra-node and horizontal inter-node data movement in terms of energy efficiency and performance.

B3

Memory technology

Integrate new memory technologies to improve capacity, bandwidth, resiliency, and energy efficiency.

B4

Scalable system software

Increase the scalability, power sensitivity, and resiliency of system software, including operating systems, runtimes, and monitoring.

B5

Programming systems

Develop programming paradigms that express fine-grained concurrency, locality, and resilience.

B6

Data management

Develop software that handles massive data volumes, including data analysis, compression, fault tolerance, and large-scale I/O.

B12

Pre/post processing

Scale visualization, in situ processing, and the preparation and analysis steps that surround large simulations.

2. Algorithmic and Methodological Bottlenecks

These bottlenecks are the core Exa-MA targets. They are the tags used by highlights, frameworks, applications, and work packages throughout the website.

ID Bottleneck Definition

ID	Bottleneck	Definition
`B7`	Exascale algorithms	Redesign algorithms to improve scalability by reducing communication, avoiding or hiding synchronization, and increasing computational efficiency on accelerators.
`B8`	Discovery, design, and decision algorithms	Move beyond single large simulations toward ensembles of many runs, as required by uncertainty quantification, parameter optimization, design, and decision workflows.
`B9`	Resilience, robustness and accuracy	Ensure computations remain correct, reproducible, and verifiable even in the presence of software and hardware errors.
`B10`	Scientific productivity	Provide scientists with tools to develop programs, run applications, prepare inputs, collect outputs, and analyze results productively on exascale systems.
`B11`	Reproducibility and replicability of computation	Provide the data, code, workflows, and practices needed to re-obtain computational results and build trust in future scientific exploration.
`B13`	Opportunity to integrate uncertainties	Integrate uncertainties directly into the core calculation, rather than treating uncertainty quantification only as a post-processing step.

B7

Exascale algorithms

Redesign algorithms to improve scalability by reducing communication, avoiding or hiding synchronization, and increasing computational efficiency on accelerators.

B8

Discovery, design, and decision algorithms

Move beyond single large simulations toward ensembles of many runs, as required by uncertainty quantification, parameter optimization, design, and decision workflows.

B9

Resilience, robustness and accuracy

Ensure computations remain correct, reproducible, and verifiable even in the presence of software and hardware errors.

B10

Scientific productivity

Provide scientists with tools to develop programs, run applications, prepare inputs, collect outputs, and analyze results productively on exascale systems.

B11

Reproducibility and replicability of computation

Provide the data, code, workflows, and practices needed to re-obtain computational results and build trust in future scientific exploration.

B13

Opportunity to integrate uncertainties

Integrate uncertainties directly into the core calculation, rather than treating uncertainty quantification only as a post-processing step.

3. Exa-MA Focus

The scientific document states that B1-B6 and B12 are tackled at the methods and algorithms level through transverse collaborations in NumPEx. Exa-MA directly addresses B7-B11 and B13 through its methods, algorithms, software libraries, methodological patterns, AI components, and benchmarked demonstrators.

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