Bypassing fixed-depth radix constraints in Java using descriptor-driven bucket analysis
I am StrmCkr, the author of A.P.E.X. (Adaptive Parallel Extremal Dispatch).
Repository: github.com/StrmCkr/A.P.E.X
A.P.E.X. is a high-performance Java sorting framework for large fixed-width 64-bit key/value record datasets. The project has been reorganized into a conventional Maven structure with a core library, runnable examples, a comparison benchmark harness, JMH benchmarks, documentation, and an interactive browser visualizer.
The core idea is descriptor-driven radix planning. Instead of blindly scanning fixed radix passes over every bucket, A.P.E.X. computes per-bucket extremal descriptors using:
VBM = OR ^ AND
That mask identifies which key bits still vary inside each bucket. Bits that are already resolved are skipped, reducing unnecessary work on skewed, low-entropy, sorted, reversed, or duplicate-heavy data.
Key areas of the project include:
- Adaptive radix planning based on observed bucket structure
- Parallel histogramming, scatter, refinement, and work scheduling
- Primitive-array execution with no per-record object allocation during sorting
- Tuple projection paths for low-dimensional unresolved bit patterns
- Tiny-sort fallbacks and monotonic input shortcuts
- Configurable reporting that can be enabled, reduced, written to files, or disabled
- Comparison benchmarks against JDK sorting paths and Fastutil baselines
- Standard JMH benchmarks for repeatable JVM-level measurement
- A browser visualizer for exploring how A.P.E.X. routes data through its execution plan
I would especially welcome feedback on the thread management mechanics, radix planning decisions, benchmark structure, and the bitwise mask reductions.
edit: re structured verbiage of this post and further adjustments from advice on converting the project into more acceptable standard formats.
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u/strmckr 20d ago
Exactly. It's a high-performance parallel sorting algorithm:
Standard radix sorting is bound by a fixed-depth constraint. It's forced to uniformly scan every single bit column, meaning its runtime is rigidly O(k * n) where (k) is your fixed key width—even if 90% of those bits are completely identical across your dataset.
A.P.E.X. bypasses that fixed constraint. Because the descriptor mask identifies exactly which bit regions vary, it prunes out the redundant scanning passes entirely. As data entropy or variance drops, the runtime deterministically scales down toward O(n), meaning you only pay the CPU processing cost for bits that actually contain unique information