execbudget mem scaling node 10.6 (#714)

Author: mgmeier

reports/2026-01-execbudget-memory-10.6.md

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+---
+title: Memory Budget Scaling -- 10.6
+slug: 2026-01-execbudget-memory-10.6
+authors: mgmeier
+tags: [benchmarking-reports]
+hide_table_of_contents: false
+---
+
+## Setup
+
+This report compares benchmarking runs for 3 different settings of the Plutus memory execution budget:
+* `10.6.1-jan26` - current mainnet memory execution budget
+* `mem-x1.5` - 1.5 x current mainnet memory execution budget
+* `mem-x2` - 2 x current mainnet memory execution budget
+
+For this comparison, we gather various metrics under the _Plutus_ workload used in release benchmarks: Each block produced during the benchmark contains
+4 identical script transactions calibrated to fully exhaust the memory execution budget. Thus, script execution is constrained by the memory budget limit
+every case. The workload produces small blocks (< 3kB) exclusively.  
+
+Benchmarking is performed on a cluster of 52 block producing nodes spread across 3 different AWS regions, interconnected using a static, restricted topology. 
+
+Identical scaling benchmarks were performed in Q1 2025 on [Node 10.2 / GHC8.10] and [Node 10.3 / GHC9.6]. This comparison aims to ascertain the past observations and conclusions still apply,
+given the most recent Node version (10.6.1, with patches for 10.6.2 backported) and its recommended compiler version GHC9.6.7.
+
+
+## Observations
+
+### Resource Usage
+
+1. Scaling the memory budget impacts Allocation Rate and Minor GCs. 1.5 x the budget results in rises of 21% - 22%; for doubling the budget the corresponding rises are 36% - 38%.
+2. These increases exhibit a slightly sublinear correlation with raising mem budget; however, in absolute terms they are much steeper (roughly 4x) compared to Node 10.3.
+3. The Node process RAM footprint is unaffected by and the effects on Process CPU usage is negligible for either scaling factor.
+4. CPU 85% span duration exhibits a slight constant increase (approx. 0.5 slots) when scaling the mem budget, regardless of the factor.
+
+Caveat: Individual metrics can't be evaluated in isolate; the resource usage profile as a whole provides insight into the system's performance and responsiveness.
+
+### Forging Loop
+
+1. Scaling the memory budget has significant impact on self adoption time only.
+2. Scaling by factor 1.5 leads to an 11ms (or 25%) increase, whereas factor 2 leads to 22ms (51%); this is fully congruent with the observations on Node 10.3.
+3. This increase is linearly correlated with raising the mem budget.
+4. With increased memory budget, counterintuitively, the time from slot start until new header announcement *decreases* slightly - by 4ms (factor 1.5) and 2ms (factor 2). This was not observed on Node 10.3.
+
+### Peer propagation
+
+1. Same as on the block producer, scaling the memory budget has significant impact on block adoption times only.
+2. Scaling by factor 1.5 leads to an 10ms (or 22%) increase, whereas factor 2 leads to 17ms (37%).
+3. Again, these increases exhibit a slightly sublinear correlation with raising the mem budget - largely congruent with the absolute increases seen on Node 10.3.
+   
+### End-to-end propagation
+
+This metric encompasses block diffusion and adoption across specific percentages of the benchmarking cluster, with 0.80 adoption meaning adoption on 80% of all cluster nodes.  
+
+1. 1.5 x the memory budget results in a minor increase of 2ms - 16ms in cluster adoption times (1% - 3%).
+2. 2 x the memory budget results in a slight 10ms - 13ms increase (2% - 3%).
+3. These increases are clearly lower (very roughly 2.5x) than those observed on Node 10.3.
+
+### Conclusion
+
+These measurements outline the headroom for raising the memory budget, along with the expected performance impact:
+1. Block adoption time is the only network metric that's affected significantly, increasing both on the forger and the peers by the same extent.
+2. These increases seem to correspond linearly at worst with raising the memory budget. This gives excellent predictability of performance impact up to a hypothetical 100% raise.
+3. Expectedly, more allocations and minor GCs take place; however, CPU and RAM usage remain nearly constant.
+4. Block diffusion is only marginally affected by changing the execution budget: Due to header pipelining, announcing and (re-)sending a block precedes adoption in most cases.
+5. As such, measurements taken with either budget adjustment *do not indicate performance risks* to the network, but clearly evidence their respective performance cost.
+6. With the exception of extra allocations, all measurements point to a recent Node version delivering equal or slightly better performance compared to 10.3 given some memory budget increase.
+
+## Attachment
+
+Full report PDF downloadable [here](../static/pdf/benchmarking/execbudget-10.6-mem_scaling.pdf).
+
+[Node 10.2 / GHC8.10]: https://updates.cardano.intersectmbo.org/reports/2025-03-execbudget-memory-10.2
+[Node 10.3 / GHC9.6]: https://updates.cardano.intersectmbo.org/reports/2025-05-execbudget-memory-10.3