Reconfigurable computing for Monte Carlo simulations: results and prospects of the Janus project
Janus Collaboration: M. Baity-Jesi, R. A. Banos, A. Cruz, L. A., Fernandez, J. M. Gil-Narvion, A. Gordillo-Guerrero, M. Guidetti, D. Iniguez,, A. Maiorano, F. Mantovani, E. Marinari, V. Martin-Mayor, J. Monforte-Garcia,, A. Munoz Sudupe, D. Navarro, G. Parisi, M. Pivanti

TL;DR
Janus is a specialized FPGA-based supercomputer optimized for large-scale, high-speed spin-glass simulations, enabling unprecedented non-equilibrium and equilibrium studies that surpass traditional computing capabilities.
Contribution
This paper introduces Janus, a novel FPGA-based architecture tailored for efficient Monte Carlo simulations of spin glasses, demonstrating significant performance improvements over conventional systems.
Findings
Simulated spin-glass dynamics over eleven orders of magnitude in time.
Achieved equilibrium states at unprecedented low temperatures and large system sizes.
Enabled simulations that would take decades on traditional architectures.
Abstract
We describe Janus, a massively parallel FPGA-based computer optimized for the simulation of spin glasses, theoretical models for the behavior of glassy materials. FPGAs (as compared to GPUs or many-core processors) provide a complementary approach to massively parallel computing. In particular, our model problem is formulated in terms of binary variables, and floating-point operations can be (almost) completely avoided. The FPGA architecture allows us to run many independent threads with almost no latencies in memory access, thus updating up to 1024 spins per cycle. We describe Janus in detail and we summarize the physics results obtained in four years of operation of this machine; we discuss two types of physics applications: long simulations on very large systems (which try to mimic and provide understanding about the experimental non-equilibrium dynamics), and low-temperature…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
