Benchmarking
Phase-Space Integration for High Energy Physics
In high-energy particle physics, phase-space integrals are used to predict cross-sections and particle decay rates for accelerator event simulations. Because these integrals often contain discontinuities and have high dimensionality, physicists typically turn to Monte Carlo simulations, but the computational cost of achieving precision is immense. This application implements the leading-order parton-level cross-section for gluon-gluon scattering.
Figure 1. Monte Carlo UQ implementation
When implemented to take advantage of UxHw technology and deployed on the Signaloid Cloud Compute Engine (SCCE), the phase-space integration exploits the UxHw technology’s capability for direct computation on probability distribution, eliminating the need for thousands of Monte Carlo iterations.
Figure 2. Signaloid UQ implementation
A phase-space integration kernel, running on a single-threaded Signaloid C0Pro-Jupiter-XXS core, achieves runtimes 63× faster than an optimized C-language Monte-Carlo-based implementation (i.e., the industry standard and status quo) running on an Amazon EC2 R7iz instance. With 95% confidence, a 7.1M-iteration Monte Carlo implementation matches the accuracy of the Signaloid UxHw-based version. This efficiency allows physicists to explore parameter spaces with a speed that turns once-intractable simulations into rapid, iterative experiments.
Key Performance Indicator
Signaloid Platform Solution
Competing Solution
Signaloid Benefit
Speed for the same uncertainty quantification accuracy.
Run existing non-Monte-Carlo code and use either the Signaloid Compute Engine's automated ingestion of distribution information, or use the Signaloid UxHw API to set program variables as probability distributions.
Run existing Monte Carlo code, or, starting from non-Monte-Carlo code, modify code to implement Monte Carlo sampling, iteration, and aggregation of the results from the Monte Carlo iterations of the computation.
63x faster execution time than 7.1M-iteration Monte Carlo, while achieving same fidelity of full distribution result.
Plot of output distribution when running on a Signaloid C0Pro-Jupiter-XXS core that provides the 63× speedup.
Plot of the output of a 7.1M-iteration Monte Carlo for this use case. Signaloid C0Pro-Jupiter-XXS core is 63× faster than this Monte Carlo, while achieving the same or better Wasserstein distance to the ground-truth (20M-iteration) Monte Carlo.
Plot of ground truth (20M-iteration) Monte Carlo.
Benchmarking Methodology
Monte Carlo simulations work by statistical sampling and therefore each multi-iteration Monte Carlo run will result in a slightly different output distribution. By contrast, Signaloid's platform is deterministic and each run produces the same distribution for a given Signaloid C0 core type.
The performance improvements are calculated by comparing Signaloid's platform with a Monte Carlo simulation of a similar quality of distribution. First, we run a large Monte Carlo simulation (about 50M iterations) on an AWS r7iz high-performance AWS instance: We use this distribution result as a baseline or ground truth reference of distribution quality. Then we calculate the performance of Signaloid's technology, and compare it with the performance of a Monte Carlo iteration count where the output distribution's Wasserstein distance (to the output distribution of the ground truth reference) is as accurate as the Signaloid-core-executed algorithm's output distribution, with 95% confidence level.
Performance data based on Fall 2025 release of Signaloid's technology.