Some Math Behind Splinter's Claims

We must guard our time. This is especially true for researchers, where system sluggishness can literally sabotage discovery and cause months or years of setbacks. Because Splinter makes some rather audacious performance claims, it should back them up with physics, not just benchmarks.

This explanation is not an attempt to start a competition with beloved industry-standard tools. We compare against SQLite and Redis because they are the only widely known frames of reference available. Splinter’s author considers both to be inspirational symphonies written in code.

But sometimes, you just need a piccolo, not an orchestra.

TL;DR: It's physics. Splinter divorced socket and mutex overhead, shrank the logic to fit entirely inside the instruction cache, and aligned I/O to prevent false sharing. It is designed to capture data first and backfill what can be extrapolated later.

The Efficiency Gap: Instruction Physics vs. Socket Tax

To justify a Passive Substrate (Splinter) over Active Middleware (Redis/SQLite), we analyze the Cycles Per Operation (CPO). This metric reveals how much "work" the CPU must perform to move a single piece of data.

There are, of course, going to be many different variances of exactly what has to happen for a connection to be initiated, what kind of security layers are in place, what kind of observation the kernel is also helping with, and what else is causing preemption on the system. So, we start as close as we can get to the actual worst case, and extrapolate a conservative best case from there.

1. The Baseline: Throttled i3-1115G4 (3.0 GHz)

At 3.0 GHz, each core has a budget of 3,000,000,000 cycles/sec.

2. The AMD Extrapolation: Proportional Scaling

On a modern AMD rig (e.g., Zen 4/5), we are no longer memory-throttled. We benefit from 128MB+ of L3 cache and significantly higher IPC (Instructions Per Cycle).

• Redis Ceiling: Even on high-end hardware, Redis is bound by the Kernel's interrupt latency. Even at 500k ops/sec, it still spends ~10,000 cycles/op due to the inescapable cost of the network stack.
• Splinter (NUMA-Pinned): With the manifold residing entirely in a massive L3 cache and pinned to a high-bandwidth Infinity Fabric lane, we expect the CPO to drop into the single digits.

3. The Math of the Claim

If we conservatively estimate the AMD rig hits 500,000,000 ops/sec (0.5 Billion), the physics becomes undeniable:

$$CPO_{AMD} = \frac{5,000,000,000 \text{ cycles/sec (est. clock)}}{500,000,000 \text{ ops/sec}} = \mathbf{10 \text{ cycles/op}}$$

This represents a 2,300x improvement in efficiency over the traditional i3 baseline (23,076 / 10).

Summary for Researchers

While traditional architectures are restricted by a Series of Jerks—our term for the erratic overhead of interrupts and context switching, Splinter functions as a Laminar Flow Substrate. On commodity hardware, Splinter already outperforms Redis by 16x and SQLite3 by 33x, despite memory throttling.

Moving to an AMD rig with NUMA-pinning will allow us to achieve a projected 10 cycles per operation. At this scale, Splinter isn't just "faster"—it is operating at the physical limit of the silicon, achieving ~2,300 times the efficiency of standard middleware by removing the "Socket Tax" entirely.

Baseline: i3-1115G4 @ 3.0GHz

Traditional Middleware (130k ops/sec): $$\frac{3 \times 10^9}{130,000} \approx 23,076 \text{ cycles/op}$$

Splinter Standard (3.2M ops/sec): $$\frac{3 \times 10^9}{3,200,000} \approx 937 \text{ cycles/op}$$

Projected: AMD Rig (NUMA-Pinned @ 5.0GHz Clock):

We're in the process of seeking/acquiring additional funding for development and plan to transition to "big iron" now that we're certain we can support weaker memory models sufficiently.

To help substantiate the need to grow the computational lab, we had to consider what we could do (GDELT is massive) with higher-resolution samplings. We knew it was high but the math was a little startling:

Splinter Conservative Estimate (500M ops/sec):

$$\frac{5 \times 10^9}{500,000,000} = 10 \text{ cycles/op}$$

The Efficiency Delta:

The jump from 23,076 cycles to 10 cycles per operation represents a 2,300x reduction in the computational energy required to audit the manifolds examined in high-resolution research.

We expect this will be the experience of teams working with massive amounts of vectors, or timing Rank 2+ tensor measurements (as we do) and invite additional use as well as scrutiny and feedback.

If you're still here, you might be interested in reading about why Splinter and Linux get along so well.