Give the Trading Path a Kernel of Its Own
Built for trading firms running market data, strategy, and order execution on Linux. Kernel tuning reduces operating-system noise on trading cores. Multikernel removes its sources: the trading path runs in its own kernel on dedicated cores and memory, while drivers, interrupts, reclaim, and everything else run in a different kernel on different cores.
Why Tuning Has a Floor
Isolated CPUs, tickless operation, RCU offload, and IRQ affinity move most noise off trading cores. They cannot move what the cores still share: the kernel itself.
Shared Kernel State
Isolated cores still use one page allocator, one reclaim path, and one set of global locks. A risk process, logger, or batch job on other cores can stall the trading thread through them.
Machine-Wide Interruptions
Code patching, kernel TLB flushes, and other machine-wide operations interrupt every CPU the kernel runs on, isolated or not. Boot flags can defer this work but not remove it.
One Kernel, One Change Window
Every strategy, feed handler, and research job shares one kernel version and one boot configuration. Changing either means a reboot outside market hours.
A Kernel Per Trading Path
Multikernel partitions each server into independent Linux kernels with dedicated cores and memory. Latency-critical paths share hardware with the rest of the system, not a kernel.
Each path runs its own kernel on its own cores and memory. Housekeeping for one kernel never runs on another kernel's cores.
Interrupt-Free Trading Cores
Interrupts from disks, management networks, and every other device belong to the device kernel on its own cores. A trading kernel sees interrupts only from the devices assigned to it.
Direct NIC Ownership
Assign an exchange-facing NIC to a trading kernel by PCI address. Its driver runs in that kernel, and its packets never pass through another one.
Memory Nobody Else Can Reclaim
A trading kernel has its own allocator, page cache, and reclaim. In our measurement under heavy memory pressure, page-fault p99 was 153x lower than on a shared kernel, with the memory-hungry job still running at full speed. Read the measurement.
Your Tuning, Per Kernel
Keep your low-latency configuration: tickless cores, timer frequency, preemption model, huge page and reclaim policy. Each kernel boots its own build and command line, so the trading kernel can be tuned hard while research runs a general-purpose one.
Operations Around Market Hours
Separate kernels also mean separate change windows and separate failure domains.
Kernel Changes Without a Reboot
LiveUpdate boots a patched or retuned kernel alongside the running one, moves the workload when you choose, and keeps the old kernel for instant rollback. Research kernels can change mid-session without touching the trading path.
Crash Containment
A panic in a research or analytics kernel stays in that kernel; the trading kernels keep running. State kept on DAXFS shared memory survives an app-kernel crash intact, so a restart does not begin cold.
Colocation and Cloud
The same stack runs on bare-metal servers in colocation and on the cloud instances you already rent for research and backtesting. No hypervisor is added in either place.
From Evaluation to Production
A three-step engagement model, measured with your own latency tooling.
Evaluation
Your feed handlers and strategy on a dedicated trading kernel, measured against your current tuned setup on your hardware, under your normal background load.
Integration
Kernel profiles, CPU, memory, and NIC layout, and boot automation, built with your infrastructure team for your colocation and cloud footprint.
Production
Rollout on a per-node subscription: validated builds, CVE stream with LiveUpdate images, and SLA-backed engineering support.