Skip to content

Original reference experiment

Historical experiment

This page preserves the original reference measurement. The later broad suite includes a longer confirmation on this graph that favors cuGraph. Use the results overview for the complete published evidence.

Measured advantage: on one matched public Graphalytics PageRank task, K GraphFuse has lower median warm compute time than native cuGraph on the same T4, lower observed total memory peaks, and accepted output accuracy. This is a scoped result. It does not establish a unique market position or global SOTA.

Same-device native cuGraph comparison

The task is datagen-7_5-fb: 633,432 vertices, 34,185,747 source edges, 68,371,494 symmetrized operator entries, global undirected unweighted PageRank, uniform start, damping 0.85, exactly ten updates. Probability output is timed. Zero truncation retains the full finite polynomial. Measurements were recorded on 9 October 2026.

Best accepted K configuration: torch.float32, unpacked, eager (capture=False). Comparator: native pylibcugraph 26.10.0 FP32 with cached graph and outgoing weight sums. Both run on Tesla T4 device 0 of the same machine. Only one GPU is used.

Metric K GraphFuse Native cuGraph Interpretation
Warm compute median 49.905806 ms 63.397649 ms 21.28% less time; 27.03% higher derived serial rate; 1.270346× ratio
Warm compute p95 49.983254 ms 67.156270 ms 25.57% lower
Compute + host output median 50.421915 ms 63.465624 ms 20.55% lower
Compute + host output p95 51.358547 ms 68.483130 ms 25.01% lower
First call after GPU preparation 61.941168 ms 70.695178 ms 12.38% lower; one observation
Sampled total device peak 1,284,702,208 B 2,970,812,416 B 56.76% lower observed total use
Sampled process RSS peak 4,920,475,648 B 7,672,205,312 B 35.87% lower observed process use
Worst per-vertex relative error 2.520975e-7 4.527438e-7 Both below the 1e-4 acceptance gate
Aggregate L1 error 4.903210e-8 4.498058e-8 K is 9.01% higher
Sampled mean board power 59.5755 W 41.8442 W K is higher; query energy was not integrated

There are 31 synchronized warm and 11 host-output samples per GPU configuration. The descriptive within-run 95% bootstrap interval for the median ratio is 1.249605–1.298629×, using 5,000 resamples and seed 20261009. Selection among configurations is not corrected in this interval. No cross-run variance is estimated. All 11 measured configurations, including the CPU product, pass the downloaded per-vertex reference; 27 original tests pass with zero skips.

Same-T4 latency, sampled memory and accuracy results

Released CPU product on the same host

Unmodified ArcadeDB 26.10.1 embedded PageRank, using the released engine and normal Graph Analytical View, records median 920.849188 ms and p95 964.995717 ms on four logical Xeon host processors. Maximum relative reference error is 4.987509e-15. Preparation takes about 164.12 s and is separate.

The ratio to the T4 result is approximately 18.45× on this machine. This is a CPU-to-GPU deployment comparison with different processors and preparation scopes; it is not a pure algorithm comparison or an industry-wide product speedup. CPU peak memory and query energy were not measured. Keep CPU and GPU execution classes visible.

Public product scores: separate hardware

The pinned vendor-published Graphalytics comparison reports Apple M5 Pro, 48 GB RAM, Temurin 25 and product-specific configurations. These are publisher results, not independently certified submissions. The methodology explains product adaptations and excluded failures.

Published product/configuration Reported task time Qualification
ArcadeDB 26.10.1 released embedded 86 ms Apple M5 Pro result; not the local Xeon result
ArcadeDB 26.11.1 development snapshot 85 ms Unreleased snapshot, separate status
ArcadeDB Docker 119 ms Container setup differs
Neo4j 2026.09.0 / GDS 6,840 ms Two GDS runs combined to match the reference
Memgraph 3.13.1 / MAGE 4,230 ms Publisher configuration
HugeGraph 2,450 ms Publisher configuration
Kuzu, archived 850 ms Archived project; lifecycle differs
ArangoDB 3.11, legacy 70,300 ms Legacy configuration; not current default

No speedup ratio divides a public Apple score by K GraphFuse's T4 timing. DuckPGQ and FalkorDB results that failed the reference are not accepted performance rankings. The raw public catalogue preserves statuses and unmeasured tasks. This original snapshot has no exact matched graph500-22 score; the later native comparison records that graph and favors native cuGraph.

Public product context with hardware and lifecycle labels

Broader suite and negative evidence

The earlier three-run suite completes 19 unique workloads, preserving 20 historical case records, 466 candidates, 283 accepted, 27 not run, and 18 exceptions; remaining candidates fail acceptance. Its strongest all-controls GPU gate requires at least 10% lower median than the best accepted GPU control, a descriptive interval above one, and no p95 regression. Zero of 19 complete workloads pass this gate. The largest completed graph has five million vertices and 34,999,980 stored entries; it is a synthetic scale control.

Some older native-library comparisons used a symmetric-output adapter or sequential query batches. They are not the same contract as the native probability result above and are not substituted for it. Large same-library ratios should not be read as public product leadership.

Reuse this claim accurately

K GraphFuse recorded 21.28% less median warm compute time, equivalent to 27.03% higher derived serial compute rate, than native NVIDIA cuGraph on the same Tesla T4 for the original ten-update Graphalytics PageRank workload. Observed total memory peaks were lower, and both outputs passed the reference check. A later longer confirmation favors cuGraph. Wider performance leadership and customer savings remain unestablished.

Attach the graph, hardware, versions, timing boundary and limitations whenever reproducing the number. Metric definitions · Methodology · Audit and reproduction · Machine-readable claims · Raw repository evidence.