federationGPUquery-planner

Federated Analytics Across Heterogeneous CPUs and GPUs: Best Practices

qqueries

2026-02-06

10 min read

Practical guide to routing query fragments across RISC‑V CPUs, GPUs, and cloud warehouses based on workload shape, cost, and data locality.

Hook: When queries miss the right engine, costs and latency explode

If your analytics platform lumps every query into a single execution path, you9re paying for it in latency, cloud egress, and frustrated analysts. Modern fleets include low-power RISC-V CPU nodes, high-throughput GPU executors, and managed cloud warehouses—each excels for different query shapes. The trick is routing the right query fragments to the right engine using a cost- and locality-aware query planner.

Executive summary — what this guide gives you

This article explains, in practical steps, how to build a federated execution strategy across heterogeneous CPUs and GPUs and cloud warehouses in 2026. You9ll get:

Decision logic to route fragments to RISC-V nodes, GPU executors, or cloud warehouses
Concrete cost and latency models (including egress and data movement)
Scheduling and connector best practices for data locality and security (sovereign clouds)
Operational recipes: profiling, pre-warming, and observability
Actionable thresholds and pseudo-code for a federated planner

Why federated heterogeneous execution matters in 2026

Two industry developments in late 202596early 2026 shaped the runtime landscape:

Tighter RISC-V + GPU integration: Efforts like SiFive9s integration with Nvidia9s NVLink Fusion enable low-latency, high-bandwidth links between RISC-V CPUs and GPUs. That reduces host/device transfer overhead and makes mixed-device pipelines viable on-prem and in sovereign clouds.
Cloud sovereignty and distributed warehouses: Providers launched region-isolated offerings (e.g., AWS European Sovereign Cloud) and customers distributed sensitive data into on-prem and sovereign stores. That makes data locality a first-class constraint for federation.

At the same time, high-value analytical workloads have bifurcated: wide-table scans and heavy aggregations (GPU-friendly) vs selective lookups and small-state transformations (CPU-friendly). A static executor choice wastes capacity and drives unpredictable spend.

Core principle: map fragment characteristics to engine strengths

Start with a simple mapping:

RISC-V CPU nodes 96 best for selective, control-heavy work: point lookups, small joins with high branching, UDFs that require scalar performance, and privacy-bounded operations on sovereign nodes.
GPU executors 96 best for wide scans, highly-parallel aggregations, vectorized transforms, and ML feature extraction.
Cloud-managed warehouses 96 best for complex distributed joins across massive managed datasets with advanced indexing or when compute-to-data is cheaper than moving data.

Measure what matters: telemetry to feed the planner

A federated planner needs live and historical metrics. Collect these signals for every storage and compute endpoint:

Data-locality metrics: region, AZ, node rack, storage tier, partition/fragment store location
Compute resource metrics: CPU cores, clock/IPC, memory bandwidth, NUMA topology, GPU SM count, GPU memory and utilization, NVLink/P2P bandwidth and latency
Network metrics: inter-node bandwidth, cross-region latency, egress per GB costs
Workload metrics: input cardinality, selectivity (predicate pushdown ratio), projected width (columns), UDF complexity
Cost metrics: $/vCPU-hour, $/GPU-hour, I/O $/GB, egress $/GB, storage retrieval costs

Persist these metrics in a time-series DB and expose to the planner through a fast feature store. Profile representative queries to derive kernel-level costs (e.g., microbenchmarks for filter, hash-join, sort on each engine).

Designing the cost model: time + money + risk

A practical cost model balances latency and monetary cost and folds in data movement and sovereignty constraints. Use this composite objective:

Minimize: alpha * estimated_latency + beta * estimated_cost + gamma * policy_penalty

Where:

estimated_latency = compute_time + transfer_time + queuing_delay
estimated_cost = compute_cost + egress_cost + storage_read_cost
policy_penalty = large fixed penalty for violating data residency, encryption, or compliance rules

Concrete submodels:

Compute time estimates

For a fragment f with input size S_bytes and operator mix (scan, map, join, agg):

compute_time_GPU 96 (S_bytes / effective_bandwidth_GPU) * op_factor_GPU

compute_time_CPU 96 (S_bytes / effective_bandwidth_CPU) * op_factor_CPU

effective_bandwidth_GPU includes device memory bandwidth, NVLink host-device transfer, and vector throughput.
op_factor captures operator complexity (e.g., joins and sorts multiply time).

Transfer time and egress

transfer_time = S_moved / link_bandwidth + link_latency

egress_cost = S_moved * egress_price_per_GB

Do not ignore serialization: parquet/ORC decoding costs vary by engine and can be a dominant factor when moving across heterogeneous hardware.

Routing heuristics and rules of thumb

Translate the model into actionable routing rules. Below are practical heuristics that work across most environments in 2026:

Rule 1 96 High selectivity early: If a filter reduces rows by > 95% and can run on RISC-V locally, push the filter to the RISC-V node. Moving 5% of the data to a GPU is often cheaper (and faster) than scanning all rows on GPU.
Rule 2 96 Wide scans and high arithmetic intensity 12013 GPU: If projected throughput > 100 GB/s of logical scan (after pruning) and operations are vectorizable, route to GPU executors12especially when NVLink or P2P is available.
Rule 3 96 Joins: co-locate or pushdown: For large hash joins, co-locate both sides on the same engine. If only one side is local and the other is in a cloud warehouse, compare egress cost vs. remote join cost; often pushing a small dimension table into the local engine beats large egress.
Rule 4 96 Sovereign data stays local: Enforce policy_penalty as infinite for queries that would violate residency. Instead, execute fragments in the sovereign RISC-V cluster or use secure remote-execution contracts with the cloud provider.
Rule 5 96 Materialize intermediates when it pays: If repeated queries reuse intermediate state across fragments, materialize (cache) on the engine with the best locality. Materialization cost is amortized across subsequent queries; prefer a local columnar cache when reuse is high.

Pseudo-code: a light federated planner

Below is a simplified planner function showing how to score engine candidates per fragment. Integrate this into your optimizer stage that emits a fragment graph.

function choose_engine(fragment f, candidates[]):
  best = null
  best_score = +inf
  for engine in candidates:
    if violates_policy(f, engine): continue
    est_time = estimate_compute_time(f, engine) + estimate_transfer_time(f, engine)
    est_cost = estimate_compute_cost(f, engine) + estimate_egress(f, engine)
    score = alpha * est_time + beta * est_cost + gamma * policy_penalty(f, engine)
    if score < best_score:
      best = engine
      best_score = score
  return best

Tune alpha and beta to your SLA vs. budget priorities. In practice, you9ll evaluate scores for a small candidate set (local RISC-V, local GPU, cloud warehouse region A, region B, etc.).

Connectors and pushdown: reduce data movement

Federation is only effective when connectors are smart. Implement the following in every connector:

Predicate pushdown 96 push filters down to storage/warehouse and return only selected rows
Projection pushdown 96 read minimal columns
Partition pruning 96 use partition metadata to avoid full scans
Statistics sync 96 periodically fetch table-level and partition-level stats to drive selectivity estimates
Bloom/min-max index hints 96 expose indexes from the storage layer so the planner can compute selectivity cheaply

For cloud-managed warehouses, rely on their cost and execution metadata (query profiles, IO estimates) via APIs to integrate warehouse-side cost estimates into the global planner.

Scheduling and execution patterns

Scheduling heterogeneous resources requires an allocator that understands resource types and affinity:

Affinity-aware scheduler: prefer NVLink-connected RISC-V + GPU pairs for fragments that cross host/device boundary frequently.
Pre-warming and kernel caching: keep hot GPU kernels hot; cold-start GPU JITs can add tens of milliseconds or more to short queries.
Adaptive batch sizing: for GPUs, choose batch sizes to saturate device memory without exceeding it. For RISC-V, keep per-core queues small to avoid context-swapping overhead.
Backpressure and graceful degradation: if GPUs are saturated, degrade by routing best-effort fragments to RISC-V nodes or warehouses based on cost controls and trade-offs.

Profiling and feedback loop: close the optimizer loop

The planner must learn from runtime. Build these feedback loops:

Log actual vs. estimated compute_time and transfer_time for each fragment.
Use lightweight ML models to predict selectivity for predicates absent precise statistics.
Continuously update op_factors per engine (scan, join, agg) from microbenchmarks and query telemetry.
Auto-tune alpha/beta using customer SLAs and observed spend curves.

Security, governance and sovereign-cloud constraints

In 2026, many customers keep regulated datasets in region-isolated clouds or local nodes. Incorporate policy constraints early in the planner:

Tag data with residency and sensitivity labels and propagate them in the fragment IR.
Disallow export of labeled data unless approved via an auditable workflow.
Use confidential compute and attestation on remote executors when required.
Prefer in-region RISC-V nodes for privacy-sensitive prefiltering before exporting anonymized aggregates to GPUs.

Operational checklist: what to instrument before going live

Before enabling dynamic federation in production, ensure you have:

End-to-end tracing from query parse 996 fragment assignment 996 execution timelines
Per-engine microbenchmarks for core operators (scan, hash-join, sort, aggregate)
Connector health checks and statistics sync (cron or event-driven)
Cost controls and hard budget caps to prevent runaway egress or GPU spend
Fallback policies (e.g., full-cloud execution) when local resources are degraded

Case study: hybrid analytics in a regulated European deployment (2026)

Context: A European fintech in 2026 stores customer PII in a sovereign AWS region and uses an on-prem RISC-V cluster for pre-processing. They also have a GPU farm for nightly heavy aggregations and a cloud warehouse for long-term analytics.

What they did:

Implemented a federated planner with a policy layer honoring residency labels.
Prefiltered PII on on-prem RISC-V nodes (selectivity > 99%) and exported only hashed aggregates to GPUs for vectorized joins and learning feature extraction. This avoided egress for raw PII.
Materialized intermediate aggregates in a local columnar cache; repeated queries hit this cache, cutting costs by 42% and latency by 3x.
Used NVLink-connected appliances for low-latency RISC-V <-> GPU transfers for latency-sensitive reports.

Outcome: The company reduced nightly GPU hours by 35% and eliminated cross-border data transfers for regulated datasets while preserving throughput.

Common pitfalls and how to avoid them

Pitfall: Blind routing to GPUs 96 Avoid sending small, selective fragments to GPUs; kernel launch/transfer overhead dominates. Mitigation: use a selectivity threshold and include kernel cold-start cost in the model.
Pitfall: Ignoring egress and serialization costs 96 Mitigation: include real egress pricing and storage read API costs in the planner; profile serialization for your formats (Parquet vs. Arrow IPC).
Pitfall: Overfitting cost model 96 Mitigation: maintain simple priors and use rolling updates; validate with A/B trials before global rollout.
Pitfall: No governance for materialized intermediates 96 Mitigation: tag cached data with sensitivity labels and enforce retention policies.

Advanced strategies and future directions

Looking beyond 2026, expect these trends:

RISC-V + GPU co-designed platforms: tighter NVLink Fusion-style integrations will further reduce host-device transfer overhead, making mixed pipelines more common.
Native hardware-aware operators: query engines will compile operators specialized to RISC-V and GPU microarchitectures at runtime, improving throughput.
Federated ML-aware planners: planners that jointly optimize analytical query execution and downstream ML training pipelines will route feature extraction to GPUs where it reduces overall pipeline cost.
Cross-provider sovereignty-aware federation: expect rich APIs enabling secure cross-region remote execution without raw data movement.

Actionable takeaway list — implementable in 30/60/90 days

30 days: Instrument selectivity and transfer-size metrics; add residency labels to tables; implement basic predicate and projection pushdown in connectors.
60 days: Add per-engine microbenchmarks and implement the composite cost model; enable rule-based routing (selectivity threshold + affinity).
90 days: Deploy a feedback loop to update op_factors, turn on adaptive scheduling, and run controlled A/B experiments comparing homogeneous vs. federated execution.

Closing: federated execution is practical now — build with observability and policies first

In 2026, with RISC-V adoption and integrated GPU links, heterogeneous federation is no longer an experimental novelty 996 it9s a practical lever to reduce latency and cost while meeting sovereignty constraints. Start small: prioritize telemetry, enforce residency policies, and use a transparent cost model. Over time, your planner9s feedback loop will reliably route fragments to the engine that makes the trade-offs you actually care about.

Call to action

Ready to evaluate heterogeneous federation on your data? Start with a profiling run across a representative query set and three candidate targets (RISC-V, GPU, cloud warehouse). If you9d like, we can help analyze your query profiles and build a cost-aware planner prototype. Contact our devops and federated-query team for a consultation and a sample planner implementation tuned to your stack.

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