Benchmarking BamScale Across Step1, GAlignments, and SeqQual Workloads

Overview

This article combines the final benchmark runs used for the current BamScale benchmark summary:

• step1 and galignments from run_20260320_133141
• seqqual from run_20260320_162359

These runs were generated with the same server-first benchmark harness, the same balanced profile family, the same deterministic case order, and the same worker/thread budget policy. Seqqual is reported separately because it includes both the fair compatibility track and the optimized compact track.

Data Loading

Benchmark Provenance

• Step1 and GAlignments run: run_20260320_133141
• SeqQual run: run_20260320_162359
• Step1/GAlignments results directory: /tmp/RtmpORhKUm/Rbuild156e2563319c/BamScale/inst/benchmarks/run_20260320_133141
• SeqQual results directory: /tmp/RtmpORhKUm/Rbuild156e2563319c/BamScale/inst/benchmarks/run_20260320_162359

Methods Rationale

This benchmark suite covers three distinct access patterns:

• step1: alignment-metadata extraction without sequence/quality materialization. This is a good proxy for BAM filtering, fragment-length profiling, and fragment-distribution QC.
• galignments: construction of alignment objects suitable for Bioconductor workflows centered on GenomicAlignments.
• seqqual: full sequence and quality extraction. This is reported in two BamScale modes:
  • fair compatibility mode, which is the closest comparator to Rsamtools::scanBam
  • optimized compact mode, which reduces internal overhead and should be interpreted as an optimized BamScale track rather than a strict apples-to-apples replacement for the compatibility path

Across all runs, the benchmark design emphasizes:

• deterministic case order
• balanced BamScale worker/thread plans
• explicit comparator baselines
• single-file and multi-file reporting
• fixed 48-thread budget for multi-file plans

Input Files

The same underlying four selected BAMs were used for both runs, with repeated files allowed to populate the 12-file multi-file benchmark set.

Reference Counts

Best-Observed Summary

BamScale-versus-Comparator Fold Change

Single-File step1

Single-File galignments

Multi-File step1

Multi-File galignments

Single-File seqqual

Multi-File seqqual

Compact-versus-Compatible seqqual

Interpretation

The benchmark shows a consistent pattern across the final runs:

• step1 and galignments are the strongest BamScale workloads.
• For these workloads, the best BamScale plans are usually low-worker, high-thread configurations, indicating that within-file multithreading contributes more than aggressively increasing the number of file workers.
• Seqqual is more difficult because sequence and quality extraction amplifies output-construction overhead.
• BamScale compact mode narrows this gap substantially and produces the strongest single-file seqqual result, but the best multi-file seqqual comparator remains faster.

In practical terms, these results support the following guidance:

• use BamScale when metadata extraction or alignment-object construction is a bottleneck in BAM-heavy Bioconductor workflows
• favor low-worker, high-thread plans when traversing one or a small number of large BAM files
• interpret compact seqqual as an optimized throughput path rather than as a strict compatibility benchmark

Session Information

## R version 4.6.0 (2026-04-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.4 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] scales_1.4.0     ggplot2_4.0.3    tidyr_1.3.2      dplyr_1.2.1     
## [5] readr_2.2.0      BamScale_0.99.10 BiocStyle_2.41.0
## 
## loaded via a namespace (and not attached):
##  [1] SummarizedExperiment_1.43.0 gtable_0.3.6               
##  [3] xfun_0.57                   bslib_0.11.0               
##  [5] Biobase_2.73.1              lattice_0.22-9             
##  [7] tzdb_0.5.0                  vctrs_0.7.3                
##  [9] tools_4.6.0                 bitops_1.0-9               
## [11] generics_0.1.4              stats4_4.6.0               
## [13] parallel_4.6.0              tibble_3.3.1               
## [15] pkgconfig_2.0.3             Matrix_1.7-5               
## [17] RColorBrewer_1.1-3          S7_0.2.2                   
## [19] S4Vectors_0.51.2            cigarillo_1.3.0            
## [21] lifecycle_1.0.5             compiler_4.6.0             
## [23] farver_2.1.2                Rsamtools_2.29.0           
## [25] Biostrings_2.81.2           Seqinfo_1.3.0              
## [27] codetools_0.2-20            htmltools_0.5.9            
## [29] sys_3.4.3                   buildtools_1.0.0           
## [31] sass_0.4.10                 yaml_2.3.12                
## [33] pillar_1.11.1               crayon_1.5.3               
## [35] jquerylib_0.1.4             BiocParallel_1.47.0        
## [37] DelayedArray_0.39.2         cachem_1.1.0               
## [39] abind_1.4-8                 ompBAM_1.17.0              
## [41] tidyselect_1.2.1            digest_0.6.39              
## [43] purrr_1.2.2                 labeling_0.4.3             
## [45] maketools_1.3.2             fastmap_1.2.0              
## [47] grid_4.6.0                  cli_3.6.6                  
## [49] SparseArray_1.13.2          magrittr_2.0.5             
## [51] S4Arrays_1.13.0             withr_3.0.2                
## [53] bit64_4.8.2                 rmarkdown_2.31             
## [55] XVector_0.53.0              matrixStats_1.5.0          
## [57] bit_4.6.0                   hms_1.1.4                  
## [59] evaluate_1.0.5              knitr_1.51                 
## [61] GenomicRanges_1.65.0        IRanges_2.47.1             
## [63] rlang_1.2.0                 Rcpp_1.1.1-1.1             
## [65] glue_1.8.1                  BiocManager_1.30.27        
## [67] BiocGenerics_0.59.3         vroom_1.7.1                
## [69] jsonlite_2.0.0              R6_2.6.1                   
## [71] MatrixGenerics_1.25.0       GenomicAlignments_1.49.0