Performance & Speed
Standardized, reproducible performance benchmarks measuring NextViper Native AOT, Bytecode VM, and Tree-Walk Interpreter execution against Python 3.14 on Linux aarch64.
Native AOT compilation eliminates variable environment lookups and leverages hardware registers.
Contiguous flat memory layout and cache-aligned SIMD row-major tensor buffers.
Zero-overhead move semantics and optimized associative container memory layouts.
Benchmark Measurement Matrix
Tested on Linux aarch64 across 5 isolated runs with CPU cache warm-up (Latency in milliseconds, lower is better)
| Benchmark Workload | NextViper Native | NextViper VM | NextViper Interpreter | Python 3.14 | Speedup |
|---|---|---|---|---|---|
| 01. Arithmetic (Prime Calculation N=5000) | 9.52 ms | 262.57 ms | 212.46 ms | 97.91 ms | 10.3× faster |
| 02. Nested Loops (1,000,000 Iterations) | 29.35 ms | 2,527.72 ms | 2,459.15 ms | 465.97 ms | 15.9× faster |
| 03. Recursive Function Calls (Fibonacci N=25) | 19.52 ms | 3,645.96 ms | 3,700.19 ms | 184.71 ms | 9.5× faster |
| 04. String Operations (Concat & Slicing) | Host Native | 40.33 ms | 33.42 ms | 111.93 ms | 3.3× faster |
| 05. Dynamic Lists (10,000 Append & Index) | Host Native | 86.32 ms | 74.26 ms | 110.40 ms | 1.5× faster |
| 06. Hash Maps (5,000 Insert & Lookup) | Host Native | 50.16 ms | 44.15 ms | 160.50 ms | 3.6× faster |
| 07. File & CSV Processing (2,000 Records) | Host Native | 133.18 ms | 68.34 ms | 332.94 ms | 4.9× faster |
| 08. Dense Matrix Multiply (60×60 Matmul + ReLU) | Host Native | 27.01 ms | 38.76 ms | 144.66 ms | 5.4× faster |
In-Place Stack Mutation (Bytecode VM)
Standard arithmetic opcodes (OP_ADD, OP_MULTIPLY, OP_LESS) directly mutate operand slots on the evaluation stack without allocating intermediate boxing variants.
SSA Register Intermediate Representation
The NextViper Native compiler translates AST nodes into a Typed Register IR, applies dead-code elimination and constant folding, and compiles directly with optimization level -O3.