Performance engineering for the predictive era
Move beyond reactive profiling. Build AI infrastructure that observes, models, and optimizes itself across every layer of the stack.
Current tools show what happened. Modern AI needs systems that predict what will happen.
NVIDIA Nsight, PyTorch Profiler, TensorBoard — powerful instruments, but designed for debugging, not for continuous optimization. As models scale to billions of parameters and training costs reach millions of dollars, reactive performance engineering becomes economically unsustainable.
Reactive Debugging
Manual profiling, post-mortem analysis. Issues discovered after they impact training runs, requiring expensive re-experimentation.
Multi-Phase Architecture
Three-tier intelligence framework that observes, models, and optimizes continuously across microsecond, millisecond, and second timescales.
Predictive Optimization
Autonomous adaptation, vendor-independent performance intelligence. Optimization decisions made in real-time based on learned patterns.
Performance modeled to your workload — not a generic checklist.
Every workload class has a distinct performance signature, so each assessment is built from the dimensions that actually govern it — plus a cross-cutting Energy & TCO model.
ML / AI
Training & inference — MFU, mixed precision (FP8/FP4), KV-cache, collective-communication scaling, fault tolerance.
Molecular Dynamics
GROMACS-class — SIMD↔cluster-pair mapping, GPU-resident execution, unified memory, PME scaling, chiplet NUMA.
Fluid Dynamics
CFD — stencil & sparse-solver bandwidth, halo exchange, mesh partitioning, pressure-solver convergence.
Weather / Climate
NWP & earth-system — the memory-bandwidth wall, GPU porting (OpenACC/Kokkos/GT4Py), spectral-transform comm, warp divergence, mixed precision, and the operational forecast window.
Engineering
FEA / crash / EM — direct vs iterative solvers, contact irregularity, memory capacity, licensing-bound throughput.
Energy · TCO
Cross-cutting — energy per useful result, cooling & power density, full-system TCO. Included with every assessment.
Find Your Workload
Take the 5-minute, workload-specific assessment.
Start AssessmentCurrent state vs MP-MS Architecture
The gap between today's reactive tools and the predictive future of AI infrastructure.
Profiling Systems
Post-mortem analysis with tools like Nsight or TensorBoard after a run completes.
Always-on observation that detects performance regressions before they impact a run.
The Observe tier replaces post-mortem profiling with continuous, microsecond-resolution telemetry feeding the model — so regressions are caught proactively, not autopsied later.
Where does your infrastructure stand?
A five-minute assessment maps your infrastructure across the dimensions that govern your specific workload, revealing the highest-impact optimization opportunities.
Begin the Assessment