Impact

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Impact

The New Physics of Software

Software used to be constrained by CPU speed. Now its constrained by power density, cooling, and water.

When execution is unpredictable, systems overprovision, burn energy, and extend thermal load. The result is a world where compute is no longer just performanceits a resource footprint. TALPs (Time-Affecting Linear Pathways) make execution measurable, predictable, and controllableso we can do more work with less waste.

Signals from the real world

These are the external constraints shaping modern computing. Our thesis: the next big performance gains come from making execution predictablenot just adding hardware.

Data center electricity
~415 TWh (2024)
IEA estimates global data centers consumed about 415 TWh in 2024 (~1.5% of global electricity). With AI acceleration, electricity demand is forecast to more than double by 2030.
Source: IEA - Energy and AI
demand trend
Water & cooling
WUE: liters / kWh
Water Usage Effectiveness (WUE) captures how much water is used per unit of energy. As power density rises, water becomes a binding constraint especially in water-stressed regions.
Source: EESI Data Centers & Water Consumption
constraint
Concrete example
1.3B gallons/year
One reported hyperscale facility consumed ~1.3 billion gallons of potable water in a yearillustrating why efficiency and predictable runtime matter at scale.
Source: NASUCA brief (compiled sources)
scale pressure
AI compute acceleration
~2x every ~6 months
Training compute for notable AI systems has accelerated dramaticallyturning power, cooling, and cost-per-result into first-order constraints.
Source: Our World in Data - training computation
compute ramp
Note: sparklines are visual trend cues. The specific numeric shapes are illustrative; the linked sources support the underlying claims.
What changed

Compute became infrastructure

Power complexity

Adding cores doesnt guarantee better outcomes. Energy, heat, and scheduling overhead can rise faster than useful work.

Water reality

Cooling isnt a footnote anymore. Water consumption becomes a regional limiter as thermal density increases.

AI variability

AI-scale workloads amplify variability and intensityexposing the cost of unknown execution paths and best-effort parallelism.

The root cause

Software cant predict itself at runtime

Weve been flying blind

  • How long will this program take for this input?
  • How many processing elements are actually optimal?
  • Which execution path will activate?
  • What is the energy cost per result?

When those answers are unknown, systems overprovision, overschedule, and overconsumeby design.

Predictable execution changes the economics

TALPs decompose software into execution pathways that can be measured, modeled, and predictedenabling systems to choose the right parallel configuration for each workload, rather than a one-size-fits-all guess.

What new physics means
Not marketing. A constraint shift: the governing limits are power, cooling, and waterso the winning software is the software that can predict and control execution.
What TALPs unlock

Make performance an optimized decision

Predict time & memory

TALPs are designed to predict runtime and resource needs for a given program and valid input dataset.

Right-size parallelism

Convert more cores into right cores, selecting the best configuration to meet latency, cost, or energy targets.

Reduce wasteful execution

Shorten sustained thermal load by minimizing wasted cycleslowering energy per result and easing cooling (and water) pressure.

From MPTs TALP summary

TALPs enable prediction of processing time and memory needs for a given program and input datasetand from that, control of system behavior to meet time or memory goals while minimizing energy use and footprint.

(See internal executive summary.)

Next

Impact is the why. Technology and Solutions are the how.

If compute is now constrained by power and water, the next frontier is predictable execution. TALPs provide the model. Our platform and workflows make it deployable.

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