Energy-efficient HPC: GPUs meet adiabatic computing
- Cedric KTORZA
- 6 days ago
- 7 min read
Energy efficiency at the heart of high‑performance computing when GPUs meet adiabatic computing. This article explains how to cut HPC power and cooling overheads today with GPU-centric architectures while preparing for adiabatic and reversible techniques that could redefine the energy floor of computation tomorrow.
At a glance
GPUs already deliver the highest performance-per-watt for parallel workloads; the next frontier is reducing data movement and heat.
Adiabatic (reversible) computing targets the fundamental limit of energy dissipation, inspiring practical strategies you can deploy now.
Combine hardware levers (DVFS, power capping, liquid/immersion cooling) with software levers (kernel fusion, reversible algorithms) for step-change gains.
At Score Group, we integrate Energy, Digital, and New Tech to align facilities, IT, and workloads for measurable efficiency improvements.
Start with metering and power baselining, then iterate with targeted optimizations and cooling retrofits; future-proof with reversible design patterns.
Why energy efficiency is the new constraint in HPC
HPC performance is no longer limited by raw FLOPS alone; it is bounded by power budgets, cooling capacity, and sustainability targets. Global electricity demand from data centres, AI, and crypto could reach 1,000 TWh by 2026 if growth continues unchecked, according to the IEA’s 2024 assessment. This makes energy-aware architectures and operations a first-order design constraint rather than an afterthought.
The fastest system is irrelevant if you can’t power or cool it sustainably within your facility envelope.
Frameworks like ASHRAE thermal guidelines inform safe operating envelopes for modern IT while enabling higher supply temperatures that reduce chiller loads. Meanwhile, the Green500 benchmark highlights performance-per-watt as a first-class metric. Together, they signal a shift: optimize the entire stack—from electrons to algorithms.
Source: IEA Data Centres 2024
Source: ASHRAE Technical Resources
Source: Top500 Green500 list
GPUs: today’s workhorse for energy-efficient throughput
GPUs excel at throughput with high memory bandwidth and massive parallelism, delivering more work per joule for vectorizable HPC and AI workloads. They also expose controls for energy-aware operations—power limiting, frequency scaling, and telemetry—allowing operators to tune for efficiency, not just peak speed.
Hardware levers that matter
Power caps and DVFS: Modestly capping power often reduces energy-to-solution with minimal time-to-solution impact, especially on memory-bound jobs. See NVIDIA DCGM for telemetry/policy control.
Memory and interconnect: High Bandwidth Memory (HBM), NVLink/Infinity Fabric, and topology-aware placement lower the dominant energy cost: data movement.
Cooling synergy: Direct-to-chip liquid and immersion cooling drastically improve heat removal, enabling denser, more efficient GPU racks. See Open Compute Project – Advanced Cooling.
Software levers for fewer joules per result
Kernel fusion and sparsity: Fewer memory round-trips and exploiting sparsity reduce energy-heavy I/O.
Mixed precision and compilation: Automatic mixed precision and graph compilers (XLA, Triton, vendor libraries) increase useful work per watt.
Energy-aware scheduling: Use power/perf counters to co-schedule jobs by thermal and power impact; MLCommons provides standardised power measurement for AI runs (MLCommons Power).
For HPC and AI organizations, tracking Green500-style metrics internally—GFLOPS/W or tokens/W—builds a culture of efficiency.
What do we mean by “adiabatic computing”?
In physics and computing theory, adiabatic or reversible computing aims to reduce energy dissipation by avoiding information erasure, which costs at least kT ln 2 energy per bit according to Landauer’s principle. Practical adiabatic circuits reuse energy rather than dumping it as heat, trading speed for lower dissipation. While mainstream, high-frequency adiabatic processors do not exist yet, the principles inform a design north star for ultra‑low‑energy computation.
Primer: Landauer’s principle
Overview: IEEE Spectrum on Reversible Computing
Research survey: Frank, Reversible Computing (arXiv)
State of the field
Adiabatic CMOS and superconducting logic (e.g., energy‑recovery or reversible gates) show promising energy reductions in prototypes at modest clock rates.
Algorithmic reversibility (reversible numerical methods, reversible neural nets) cuts memory and checkpoint costs today, aligning with the adiabatic ethos: less information loss, less energy.
The takeaway: adiabatic computing is a long‑term hardware direction. But its ideas pay dividends now when you design to minimize information destruction and data movement.
Where GPUs meet adiabatic ideas—what you can use today
The most expensive watt in HPC is the one spent moving data. Adiabatic thinking pushes us to redesign computations and systems to reduce irreversible data loss (and thus re‑computation) and to move data less often.
Reversible algorithms and memory minimisation
Reversible deep learning: Architectures like RevNets reconstruct activations on the fly, cutting memory traffic during backpropagation and therefore energy. See early formulations in Chen et al., 2016 and follow‑ups on reversible nets.
Checkpointing strategies: Optimal checkpoint intervals minimise re-computation and I/O—tune per model and hardware.
Reversible numerics: In PDE solvers and molecular dynamics, symplectic and time‑reversible integrators preserve information structure, often enabling longer stable steps.
Data locality as a first-class objective
Keep work close to HBM: Tile and block to maximize HBM reuse; avoid spilling to host memory.
NVLink/NUMA awareness: Place communicating processes on fast links; pin memory to avoid cross‑socket traffic.
Storage prefetching and compression: Compress once near storage and decompress on‑GPU to reduce PCIe and network energy—validate with power telemetry.
Thermal and power orchestration
Power-aware scheduling: Group high-TDP jobs with adequate cooling headroom; stagger thermal peaks.
Set-point optimization: Run warmer water loops where safe per ASHRAE to reduce compressor energy.
Advanced cooling retrofits: Direct‑to‑chip liquid or immersion cooling reduce PUE and enable denser GPU clusters. See OCP Advanced Cooling.
Practical energy levers across the HPC stack
Below is a practical map of techniques you can combine. Start with metering and quick wins; iterate towards deeper redesigns inspired by reversibility.
Energy levers and adoption path
Designing an energy‑efficient HPC stack with Score Group
At Score Group, we bring a tripartite approach that aligns energy, digital infrastructure, and new technologies so you can measure, optimize, and scale efficiently.
Noor Energy — Intelligent energy and cooling
Smart energy management: metering, monitoring, and optimization of consumption across rooms and racks.
Building and plant control (GTB/GTC): integrate chiller loops, pumps, CRAHs/CDUs, and free cooling to match IT load.
Renewables and storage: solar and on‑site storage to shave peaks and enhance resilience.
Sustainable mobility: EV charging for sites that include fleet operations.
Noor ITS — Digital infrastructure as a transformation core
Data center design and optimization: network fabrics, systems, and lifecycle maintenance aligned with power/cooling envelopes.
Cybersecurity: protect HPC clusters and data pipelines end‑to‑end.
Cloud and hosting (private/public/hybrid): right‑place workloads to match energy profiles and resilience needs.
PRA/PCA: plan for continuity within power and thermal constraints.
Noor Technology — New tech to stay ahead
AI enablement: performance‑per‑watt tuning, model compression, and energy‑aware MLOps.
RPA and orchestration: automate energy‑aware scheduling and telemetry-driven actions.
Smart Connecting (IoT): dense sensor networks and real‑time telemetry for thermal and power insights.
Application development: dashboards and decision tools for operators and leadership.
Discover our integrated approach: Score Group – Where efficiency meets innovation.
A practical roadmap: from baselines to breakthroughs
Measure first: instrument GPU power (DCGM), node-level power, PUE/DCiE, and thermal maps. Establish energy-to-solution baselines per workload.
Quick wins (weeks): tune DVFS/power caps; enable mixed precision; schedule for topology; raise safe cooling set points per ASHRAE guidance.
Deep dives (1–3 months): adopt kernel fusion and sparsity; implement checkpointing; pilot liquid cooling on the densest racks.
Strategic bets (quarter+): integrate reversible algorithms where viable; evolve toward immersion cooling and renewable-backed supply; codify energy SLAs in scheduling.
FAQ
What is the difference between adiabatic computing and adiabatic cooling in data centers?
Adiabatic computing refers to reversible, energy‑recovering logic that approaches the theoretical minimum energy dissipation per operation by avoiding information erasure (Landauer’s principle). It is a computing paradigm. Adiabatic cooling in facilities typically means evaporative or other techniques that reduce air temperature with minimal compressor work. They are separate concepts—one is about the physics of computation, the other about HVAC design. Both can contribute to lower total energy, but they operate at different layers of the stack.
Can GPUs really lower total energy use, or do they just finish faster?
Both effects can be true. GPUs deliver higher performance-per-watt on parallel workloads, so they often reduce energy-to-solution compared to CPUs by completing tasks faster with less total energy. The key is tuning: modest power caps and DVFS can decrease joules per result for memory‑bound jobs, while software techniques (kernel fusion, mixed precision) reduce data movement and compute waste. Measure with on-device telemetry (e.g., NVIDIA DCGM) and system power meters to validate energy savings per workload.
How close are we to practical adiabatic processors for HPC?
Research in adiabatic/reversible logic is active, including energy‑recovery CMOS and superconducting approaches, but mainstream high‑frequency, general‑purpose processors based on reversible logic are not yet available. For the near term, the most practical path is to adopt adiabatic‑inspired techniques—reversible algorithms, checkpointing strategies, and data locality optimisations—on existing GPU platforms. Follow developments via overviews such as IEEE Spectrum and academic surveys to track readiness and potential pilot opportunities.
What metrics should I track to manage HPC energy efficiency?
Track both infrastructure and workload metrics. Facility: PUE, water temperature set points, cooling plant efficiency. IT: node and rack power, GPU power/temperature, utilization, and thermal hotspots. Workload: energy-to-solution, performance-per-watt (e.g., GFLOPS/W or tokens/W), and energy breakdown by compute vs. data movement. Benchmarks like the Green500 and MLCommons Power provide reference methodologies. Establish baselines, then compare before/after for each optimization you deploy.
Key takeaways
GPUs are today’s best lever for throughput per watt, especially when tuned for memory locality and right‑sized power.
Adiabatic computing is a long‑term hardware path, but its principles guide immediate wins: less information loss, less data movement.
Cooling is strategy, not plumbing—liquid and immersion unlock denser, more efficient GPU clusters.
Instrumentation is non‑negotiable: measure energy-to-solution per workload and iterate.
Score Group aligns Energy, Digital, and New Tech so improvements compound across facilities, IT, and software.
Ready to align performance with sustainability? Connect with us at Score Group to design and implement your energy‑efficient HPC roadmap.
