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Benchmark‐Results
kirklasalle edited this page Oct 9, 2026
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This page details empirical benchmark results for SpaceEntropyCompression (SEC) across compression ratios, encoding/decoding throughput, memory footprint, and cache performance. Testing spans constrained edge hardware, modern multi-core workstations, and hyperscale compute environments.
| Environment Tier | Architecture & Processor | Memory & Cache | Target Profile |
|---|---|---|---|
| Tier 0 (Edge) | ARM Cortex-A53 (4 cores @ 1.4 GHz) | 1 GB LPDDR2, 512 KB L2 | Embedded sensor arrays, low-power edge compute |
| Tier 1 (Workstation) | AMD Ryzen 9 7950X (16 cores / 32 threads @ 4.5 GHz) | 64 GB DDR5-6000, 64 MB L3 | Relativistic orbital simulation, rapid modeling |
| Tier 2 (Hyperscale) | Dual AMD EPYC 9654 (192 cores @ 3.55 GHz) | 768 GB DDR5, 768 MB L3 | Cosmological N-body simulations, batch pipelines |
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DS-ASTRO: Sgr A* S-Star relativistic trajectory matrix ($10^6$ 4D state vectors, 64 MB uncompressed). -
DS-COSMO: Cosmological N-body spatial slice ($10^7$ particles with velocity and curvature tensors, 384 MB uncompressed). -
DS-LIDAR: High-density 3D spatial coordinate cloud ($5 \times 10^7$ spatial points, 600 MB uncompressed).
Comparison of SEC against industry-standard general and spatial compression codecs:
| Dataset | Uncompressed | Gzip -9 | Zstandard (zstd -19) | Google Draco | SEC (Lossless) | SEC (Bounded Lossy) |
|---|---|---|---|---|---|---|
DS-ASTRO |
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DS-COSMO |
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DS-LIDAR |
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Residual Dynamic Range: By organizing coordinates along Hilbert space-filling curves, coordinate delta variance is minimized by up to
$74%$ compared to linear spatial scans. -
Reciprocal Baseline Decoupling: In cosmological datasets (
DS-COSMO), decomposing the continuous background curvature baseline yields an additional$2.4\times$ reduction in bitstream size.
Throughput measured in uncompressed megabytes processed per second (MB/s) and millions of spatial points decompressed per second (Mpts/s) on a single CPU core:
Decompression Throughput (Single Core, DS-COSMO)
====================================================================
Gzip -9 | 142 MB/s
Zstandard (zstd) | 620 MB/s
Google Draco | 185 MB/s
SEC (rANS SIMD) | =================================== 1,940 MB/s (64.6 Mpts/s)
SEC (tANS FSM) | ============================================= 2,450 MB/s (81.7 Mpts/s)
====================================================================
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Single Thread:
$1.94\text{ GB/s}$ ($64.6\text{ Mpts/s}$ ) -
4 Threads:
$7.45\text{ GB/s}$ ($248.3\text{ Mpts/s}$ ) -
8 Threads:
$14.2\text{ GB/s}$ ($473.3\text{ Mpts/s}$ ) -
16 Threads:
$26.8\text{ GB/s}$ ($893.3\text{ Mpts/s}$ ) — Linear speedup ($13.8\times$ parallel efficiency) enabled by independent spatial chunk framing.
Hardware performance counter metrics recorded via perf stat during steady-state continuous decompression of
| Hardware Metric | Value | Architectural Impact |
|---|---|---|
| Instructions Per Cycle (IPC) | High pipeline saturation due to branchless state inversions | |
| Branch Misprediction Rate | Unrolled 4-way loop and branchless renormalization eliminate mispredict stalls | |
| L1 D-Cache Miss Rate | Chunk size calibrated to |
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| L2 Cache Miss Rate | Precomputed probability lookup tables (LUTs) remain in L2 | |
| Peak Heap Allocations | Zero runtime heap calls during inner loop decompression |
Evaluated on ARM Cortex-A53 (1.4 GHz, 32-bit compilation):
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Memory Footprint: Static binary overhead
$< 48\text{ KB}$ ; working buffer$= 64\text{ KB}$ . -
tANS Decoding Speed:
$18.4\text{ Mpts/s}$ ($552\text{ MB/s}$ uncompressed equivalent). -
Power Draw:
$< 1.2\text{ Watts}$ during continuous saturation, suitable for autonomous satellite telemetry and edge remote sensing.