Building and deploying the Best platforms for high-frequency trading for ultra lowest latency

Building and Deploying an Ultra-High-Performance but Best High Frequency Trading System: A Comprehensive Guide

Part 1: Introduction to Ultra-High-Frequency Trading (HFT) Systems

Built the best platforms for High-Frequency Trading (HFT) represents the pinnacle of financial technology, a domain where profitability is measured in microseconds and nanoseconds. An HFT system is an automated trading platform that utilizes powerful computers and complex algorithms to transact a large number of orders at extremely high speeds. These systems operate on timescales far beyond human capability, executing strategies that capitalize on fleeting market opportunities, often lasting for only a fraction of a second.

BIG HIGHLIGHTS:

Scalability Features:

16 Worker Threads: Parallel processing across multiple cores

Load Balancing: Messages distributed by symbol_id hash

Real-Time Priority: Critical threads run with SCHED_FIFO

Minimal Memory Allocation: Pre-allocated pools and stack-based operations

Hardware TSC: Direct CPU timestamp counter for ultimate timing precision

Performance Targets:

1M+ messages/second sustained throughput

Sub-microsecond latency for critical path operations

Linear scalability with CPU core count

99.9% uptime with graceful degradation

The system is designed to fully utilize modern multi-core CPUs and can easily scale to handle millions of messages per second while maintaining low latency and high reliability.

The core challenge in HFT is the relentless pursuit of speed. This is not merely about having a fast computer; it's a holistic engineering discipline that encompasses everything from the physical location of servers (co-location in exchange data centers) to the silicon-level optimization of code. The journey has evolved from milliseconds (thousandths of a second) to microseconds (millionths) and now into the realm of nanoseconds (billionths). At these speeds, even the speed of light becomes a tangible constraint, dictating the length of fiber optic cables between systems.

Building such a system presents a trifecta of challenges:

1. Market Data Deluge: Modern exchanges disseminate enormous volumes of data—quotes, trades, and order book updates—amounting to millions of messages per second. The system must be able to ingest, process, and act on this firehose of information without falling behind.

2. Latency: Every nanosecond of delay, or "latency," is a potential loss of opportunity. This includes network latency (data traveling over wires), and processing latency (the time the application takes to make a decision). Minimizing this "tick-to-trade" latency is the primary goal.

3. Reliability and Determinism: The system must be not only fast but also predictably fast and robust. A system that is fast on average but has unpredictable latency spikes (jitter) is unreliable. It must perform its function with deterministic, repeatable timing, and handle failures gracefully without causing market disruption.

This document provides a comprehensive guide to building and deploying the ultra_hft_5m_mps system, a C++ application designed from the ground up to tackle these challenges. Its goal is to achieve a throughput of over 5 million messages per second while maintaining microsecond-level latencies. We will walk through every stage of its lifecycle: from system preparation and dependency installation to advanced build configurations, deep system-level optimizations, production deployment, and performance validation. At the heart of this guide is a detailed exploration of the C++ source code, revealing the techniques that give the system its ultra-high-performance characteristics.

Part 2: System Architecture and Design Philosophy

The design of an HFT system is governed by a principle known as "Mechanical Sympathy"—the practice of designing software that works in harmony with the underlying hardware. Instead of fighting the hardware with abstractions, we embrace its nature to extract maximum performance.

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Core Architectural Principles

• Hardware and Software Co-design: The software is not written in a vacuum. It is designed with a specific hardware profile in mind: multi-core CPUs with deep cache hierarchies, NUMA (Non-Uniform Memory Access) architectures, and fast network interfaces.

• Kernel Bypass: The operating system kernel, while providing useful abstractions, is a major source of latency and jitter. High-performance systems aim to bypass the kernel for critical data paths, communicating directly with hardware (e.g., network cards) from user space. While this guide's application uses the standard kernel networking for simplicity, the system optimizations aim to reduce kernel overhead as much as possible.

• Zero-Copy & Minimal Memory Operations: Copying data is expensive. The architecture is designed to minimize or eliminate data copies. Data is processed in place wherever possible. Memory allocations on the critical path are forbidden; all necessary memory is pre-allocated.

Component Breakdown and Data Flow

A typical HFT system can be broken down into a pipeline of specialized components. The journey of a market data packet through our system looks like this:

1. Market Data Ingress: A dedicated thread (or process) listens on the network, receiving raw market data packets. Its sole job is to timestamp the packet with the highest precision possible and place it into a communication channel (a lock-free ring buffer) for the worker threads.

2. Order Book Processing: Worker threads consume the data from the ring buffer. They parse the messages and use them to update their internal representation of the market's limit order book for various financial instruments. The system must maintain a real-time, accurate view of the market.

3. Strategy Engine & Model Implementation: With an up-to-date order book, the strategy engine runs its algorithms. This is where proprietary logic resides. For instance, it might detect patterns, such as large hidden orders, or use quantitative models like the SABR model to calculate volatility surfaces and identify pricing discrepancies. The model's parameters might be dynamic, adjusting to market conditions (e.g., the volatility parameter ν changing on a Tuesday afternoon).

4. Risk Management Core: Before any order is sent, it must pass through a rigorous risk check. This component continuously calculates portfolio-level risk metrics like Delta, Gamma, and Vega. It monitors exposure and may automatically trigger hedging trades if risk limits are breached. It also calculates metrics like adverse selection probability (μ) based on recent trade flow to adjust quoting strategy.

5. Execution Engine: Once a decision is made and approved by the risk system, the execution engine is responsible for generating and sending orders to the exchange. This involves formatting the order in the exchange's native protocol (e.g., FIX) and sending it out through the network interface. This must be an atomic, all-or-nothing operation.

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This entire pipeline constitutes a critical feedback loop: Market Data -> Model -> Execution -> Risk -> Market Data. The "secret sauce" of an HFT firm lies not in a single algorithm, but in the perfect, high-speed, and reliable orchestration of these complex dependencies.

Part 3: Setting Up the Battlefield: System Preparation and Requirements

Before we can build our high-performance engine, we must prepare the environment. The provided scripts automate this process, ensuring the system has the necessary capabilities and dependencies.

3.1. Project Structure

A well-organized project is crucial for managing complexity. The ultra_hft_system directory provides a clean separation of concerns:

bash

ultra_hft_system/

├── build/         # Build artifacts will be generated here

├── scripts/       # Automation scripts (check, install, build, deploy)

├── config/        # Runtime configuration files

├── deploy/        # Files related to production deployment

└── test/          # Test cases and performance testing results

3.2. System Requirements Check

The scripts/check_system_requirements.sh script is the first step. It doesn't install anything but verifies that the machine is suitable for HFT workloads. Let's dissect its key checks:

• CPU Information:

  • Cores: HFT systems are inherently parallel. The script warns if there are fewer than 8 cores, as modern systems rely on dedicating specific cores to specific tasks to eliminate context-switching overhead.

  • Model & Frequency: A modern CPU with a high clock speed reduces the time taken for each instruction.

  • CPU Features (AVX2, FMA, etc.): These are SIMD (Single Instruction, Multiple Data) extensions. AVX2 allows the CPU to perform the same operation on multiple data points (e.g., eight floating-point numbers) with a single instruction, providing a massive performance boost for numerical calculations. [1][2] FMA (Fused Multiply-Add) further speeds this up by combining two operations into one.

• Memory Information:

  • Total Memory: HFT systems often load large datasets and require significant memory for order books and other state. 16GB is a reasonable minimum.

  • NUMA (Non-Uniform Memory Access): This is one of the most critical aspects for low-latency systems. [3][4] On multi-socket or even modern single-socket CPUs, memory is partitioned into "nodes," each local to a group of cores. Accessing local memory is significantly faster than accessing remote memory (memory on another CPU's node). The script checks for a NUMA topology, as our application will explicitly manage memory locality to ensure threads always access local memory.

• Network Interfaces: The script checks the speed of network interfaces. For HFT, 10GbE, 25GbE, or even faster connections are standard.

• OS and CPU Governor:

  • Kernel: A modern Linux kernel is essential. Some HFT firms use specially patched real-time kernels (PREEMPT_RT) to further reduce jitter.

  • CPU Governor: Modern CPUs can dynamically change their frequency to save power. This is disastrous for latency, as it can take milliseconds to ramp up to full speed. The script checks if the governor is set to performance, which locks the CPU at its maximum frequency.

• Huge Pages: By default, the OS manages memory in small 4KB pages. This can lead to performance overhead due to the large number of pages the CPU's TLB (Translation Lookaside Buffer) has to manage. Huge Pages allow the OS to use much larger page sizes (e.g., 2MB or 1GB), reducing TLB misses and improving memory access performance.

3.3. Dependency Installation

The scripts/install_dependencies_ubuntu.sh and scripts/install_dependencies_centos.sh scripts automate the installation of the toolchain and libraries.

• Build Tools:

  • build-essential, cmake, ninja-build: A modern C++ compiler (GCC 11+ is specified), CMake for managing the build process, and Ninja for its high-speed parallel build execution.

  • ccache: Caches compilation results to dramatically speed up subsequent builds.

• Performance Libraries:

  • libeigen3-dev: A high-performance C++ template library for linear algebra, useful for model calculations.

  • libnuma-dev: The development library for NUMA control, allowing us to pin threads and memory to specific NUMA nodes. [3]

  • libsqlite3-dev, liblz4-dev, zlib1g-dev: General-purpose libraries for data storage, compression, etc., which might be used for non-critical path tasks like logging or configuration loading.

• Monitoring and Debugging Tools:

  • linux-tools-generic, perf: perf is an indispensable profiling tool on Linux for identifying performance bottlenecks.

  • numactl, htop, sysstat: Tools for observing NUMA placement, system load, and other performance metrics.

  • valgrind, gdb, strace: Essential tools for memory debugging, general debugging, and tracing system calls.

Part 4: The Build Process: Forging the High-Performance Engine

Compiling the code is not just about turning C++ into an executable; it's about instructing the compiler to perform aggressive optimizations tailored to our specific hardware.

4.1. CMake Build Configuration

The CMakeLists.txt file is the blueprint for our build. It's meticulously crafted for performance.

• C++ Standard: It enforces C++20, which provides modern features like concepts, ranges, and improved concurrency primitives that can lead to cleaner and more performant code.

• CPU Architecture Detection: It cleverly attempts to compile a small piece of code using AVX2 and FMA instructions. If successful, it adds the -march=native -mavx2 -mfma flags. -march=native is critical: it tells the compiler to generate code optimized for the specific CPU it's being compiled on, unlocking all available instruction sets. [5] If AVX2 isn't available, it falls back to the older SSE4.2.

• Compiler-Specific Optimizations: 

  • -O3: The highest level of standard optimization.

  • -DNDEBUG: Disables assertions and other debug code.

  • -ffast-math: Allows the compiler to make floating-point optimizations that might slightly bend IEEE 754 rules but are significantly faster. This is acceptable for many financial models where absolute precision to the last bit is less important than speed.

  • -funroll-loops: Unrolls loops to reduce branching overhead.

  • -flto: Enables Link-Time Optimization (LTO). This allows the compiler to perform optimizations across different source files (translation units) at the final linking stage, enabling powerful inlining and code elimination that wouldn't otherwise be possible.

• Dependency Management: It uses find_package and pkg_config to locate all the necessary libraries (Eigen, NUMA, etc.) and ensures they are correctly linked.

• Compile Definitions: 

  • EIGEN_NO_DEBUG: Disables expensive checks within the Eigen library.

  • EIGEN_DONT_VECTORIZE=0: Explicitly enables Eigen's SIMD vectorization capabilities.

4.2. Build Scripts

The build scripts provide a user-friendly interface to the complex CMake configuration.

• scripts/build_cmake.sh: This is the primary build script. It creates a separate build directory (e.g., build_release) to keep the source tree clean. It invokes CMake and then uses ninja to run the compilation in parallel, using all available CPU cores for maximum speed.

• scripts/build_direct.sh: This script offers a more raw, direct approach, calling the compiler (g++) with an explicit list of flags. This is useful for fine-tuning and experimenting with compiler options without the indirection of CMake.

• Optimization Verification: Both scripts intelligently use objdump -d to disassemble the final binary and grep for specific SIMD instruction mnemonics like vfmadd (FMA) or vmovup (AVX). This provides concrete proof that the compiler has successfully vectorized the code, a crucial sanity check.

Part 5: The C++ Core: main_5m_mps.cpp - The 5 Million Messages/Second Engine

Here we present the heart of the system. The following C++ code is a functional, albeit simplified, representation of an ultra-high-performance trading application. It demonstrates the key techniques discussed throughout this guide.

5.1. Core Design Philosophy

The code is designed with the following principles in mind:

• Separation of concerns: A dedicated producer thread mimics network data arrival, while a pool of worker threads handles the processing.

• Data-oriented design: Data structures are laid out to be cache-friendly.

• Lock-freedom: The critical communication path between the producer and consumers uses a lock-free ring buffer to eliminate contention. [6][7]

• Explicit hardware control: Threads are pinned to specific CPU cores, and memory is allocated on specific NUMA nodes.

5.2. Handling Millions of Messages per Second: The Lock-Free Ring Buffer

At the core of our inter-thread communication is a lock-free Single-Producer, Single-Consumer (SPSC) ring buffer. We use one of these for each worker thread. This data structure allows one thread (the producer) to pass data to another (the consumer) without ever using mutexes or other locking mechanisms that would cause threads to block and introduce latency. [6]

It works by using atomic operations and careful memory ordering. The producer updates a write_idx and the consumer updates a read_idx. The key is that only one thread ever writes to each index, eliminating race conditions. The C++ std::atomic library provides the necessary tools.

• memory_order_relaxed: Used when no ordering constraints are needed, offering the highest performance.

• memory_order_release: Ensures that all previous writes from the producer thread are visible before the index is updated.

• memory_order_acquire: Ensures that after the consumer reads the updated index, it also sees all the data written by the producer.

5.3. Managing Complex Dependencies: A Multi-Threaded Architecture

The application is structured into distinct, pinned threads, each with a dedicated role.

• The Ingress (Producer) Thread: This thread simulates a network card receiving data. It is pinned to a single, isolated CPU core. Its only job is to generate messages, timestamp them, and enqueue them into the correct worker's ring buffer (distributing load via a simple hash of the symbol ID). It runs at the highest possible real-time priority (SCHED_FIFO) to ensure it is never preempted by other, less critical processes.

• The Worker Threads: These are the workhorses of the system. Each worker is pinned to its own core on a specific NUMA node. [3][8] All memory the worker needs, including its ring buffer and order book data, is allocated directly on that same NUMA node using numa_alloc_onnode. This guarantees the fastest possible memory access speeds. The main loop of a worker is simple:

  1. Dequeue a batch of messages from its ring buffer.

  2. For each message, update the relevant order book.

  3. Perform some SIMD-optimized calculation (a dummy VWAP calculation is shown).

  4. Record latency statistics.

5.4. Maintaining Nanosecond-Level Synchronization: Timing

For measuring latency, standard clocks like std::chrono::high_resolution_clock are often insufficient as they can have overhead and may not be monotonic. For the ultimate precision, we use the CPU's own Time-Stamp Counter (TSC) via the __rdtsc() intrinsic. This instruction reads a 64-bit counter that is incremented every clock cycle. By reading it at the start (ingress) and end (after processing) of our pipeline, we can measure latency with nanosecond precision. The code includes a simple calibration function to convert TSC ticks into nanoseconds.

5.5. The Full main_5m_mps.cpp Source Code

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Part 6: System Optimization and Tuning for Production

Building a fast binary is only half the battle. The environment it runs in must be tuned to eliminate sources of latency and non-determinism. The scripts/optimize_system.sh script is a powerful tool for this, but it must be used with caution as it makes deep, system-wide changes.

6.1. The optimize_system.sh Script: A Line-by-Line Analysis

This script requires root privileges to modify kernel parameters.

• CPU Governor and Idle States:

  • cpupower frequency-set -g performance: As discussed, this locks the CPU at its highest frequency, preventing latency spikes from frequency scaling.

  • echo 1 > /sys/devices/system/cpu/cpu*/cpuidle/state*/disable: This disables deeper CPU sleep states (C-states). While sleeping saves power, waking up can take microseconds or even milliseconds, an unacceptable delay.

• CPU Isolation: This is one of the most effective techniques for creating a quiet environment for the application.

  • isolcpus=... nohz_full=... rcu_nocbs=...: These are kernel boot parameters set in the GRUB configuration.

    • isolcpus: Tells the Linux scheduler to not run any general system tasks on these cores. They become reserved exclusively for applications we manually assign to them.

    • nohz_full: Disables the scheduler's periodic timer tick on these cores. This tick, while necessary for general operation, is a source of jitter. Disabling it allows our application to run uninterrupted.

    • rcu_nocbs: Reduces the workload related to Read-Copy-Update (RCU) callbacks on the isolated cores.

  • A reboot is required for these parameters to take effect.

• Memory Optimizations:

  • vm.nr_hugepages = 2048: Allocates a pool of 2048 Huge Pages at boot.

  • vm.swappiness = 1: Drastically reduces the kernel's tendency to swap memory pages to disk. Swapping is a performance killer and should be avoided at all costs for a latency-sensitive application.

  • vm.overcommit_memory = 2: Configures the kernel's memory overcommit behavior, which can be important for applications that manage large pre-allocated memory pools.

• Network Optimizations:

  • net.core.rmem_max, net.core.wmem_max, etc.: These sysctl settings increase the size of kernel network buffers. This helps prevent packet loss during high-volume bursts of market data.

  • net.ipv4.tcp_congestion_control = bbr: Sets the TCP congestion control algorithm to BBR (Bottleneck Bandwidth and Round-trip propagation time), which can offer better performance on modern networks.

• IRQ Affinity and Transparent Huge Pages (THP):

  • systemctl stop irqbalance: The irqbalance daemon automatically distributes hardware interrupts (IRQs) across CPU cores. We disable it to manually pin critical IRQs (like from our network card) to specific, non-application cores, preventing the trading application from being interrupted.

  • echo never > /sys/kernel/mm/transparent_hugepage/enabled: THP is a system that tries to automatically use huge pages for applications. However, its allocation process can introduce significant latency spikes. For HFT, it's better to disable THP and manage huge pages explicitly.

The script creates a persistent configuration file (/etc/sysctl.d/99-ultra-hft.conf) so that many of these settings survive a reboot.

Part 7: Deployment and Operations

A production system needs to be robust, manageable, and secure. The scripts/deploy_production.sh script automates the creation of a production-ready environment.

7.1. Production Deployment with deploy_production.sh

• Dedicated User and Directories: It creates a non-root user (hft) and a dedicated home directory (/opt/ultra_hft). Running the application as a non-root user is a critical security practice.

• Systemd Service (ultra-hft-5m-mps.service): This is the modern standard for managing services on Linux. [9][10] The generated service file is a masterclass in configuration for a high-performance application:

  • [Unit]: Defines dependencies, ensuring the service starts only after the network is ready.

  • [Service]:

    • User, Group: Specifies the unprivileged user to run as.

    • ExecStart: The command to run the binary.

    • Restart=always: Automatically restarts the service if it crashes.

    • CPUSchedulingPolicy=1 (SCHED_FIFO), CPUSchedulingPriority=50: Sets the real-time scheduling policy, giving the process priority over normal tasks. This is the systemd equivalent of what our C++ code does with sched_setscheduler.

    • IOSchedulingClass=1 (best-effort), IOSchedulingPriority=4: Sets a higher I/O priority.

    • LimitMEMLOCK=infinity: Allows the process to lock an unlimited amount of memory into RAM, preventing it from being swapped out. This is crucial for real-time applications.

    • Security Hardening: NoNewPrivileges=true, PrivateTmp=true, ProtectSystem=strict are modern systemd options that sandbox the service, reducing its potential attack surface. [9]

• Log Rotation: It sets up logrotate to manage log files, preventing them from filling up the disk.

• Monitoring Script: A simple monitor.sh script is provided for quick health checks, showing process status, CPU/memory usage, and recent logs.

7.2. Running and Monitoring the System

The BUILD_AND_RUN.md file summarizes the key commands for operators:

• Starting/Stopping: sudo systemctl start|stop|status ultra-hft-5m-mps

• Monitoring Logs: journalctl -u ultra-hft-5m-mps -f provides a real-time stream of the application's logs.

• Manual Execution: For debugging, it shows how to run the application directly with tools like taskset (to manually set CPU affinity) and nice (to adjust priority).

Part 8: Performance Testing and Validation

"If you can't measure it, you can't improve it." The scripts/performance_test.sh script provides a rigorous framework for benchmarking the application and collecting a wealth of diagnostic data.

8.1. The performance_test.sh Script

This script orchestrates a comprehensive test run:

1. Collects System Info: It records the exact state of the system (kernel, CPU, etc.) to ensure tests are reproducible.

2. Starts System Monitors: It runs iostat, vmstat, and sar in the background to log disk I/O, memory usage, and per-core CPU utilization throughout the test.

3. Runs the Application under Profilers: It launches the trading application using three powerful tools simultaneously:

   • timeout: To ensure the test runs for a fixed duration.

   • strace -c: To count all system calls made by the application. An HFT application on its critical path should make very few system calls.

   • perf record -g: The most powerful tool in the arsenal. It samples the application's call stack thousands of times per second, building a detailed profile of where CPU time is being spent.

4. Generates Reports: After the test, it uses perf report to generate a human-readable performance profile, which can pinpoint "hot" functions that need optimization. It also analyzes the application's own log output to extract the final throughput metrics.

5. Archives Results: All logs and reports are bundled into a single tar.gz archive for later analysis.

8.2. Interpreting the Results

The output of this script is invaluable.

• The perf_report.txt will show which functions are consuming the most CPU cycles. In a well-optimized system, you'd expect to see most of the time spent in the core worker loop, not in library functions or system calls.

• The syscalls.log from strace should show a minimal number of system calls during the main processing phase.

• The cpu_usage.log from sar will confirm that the producer and worker threads are running at 100% on their pinned cores, and that the isolated cores are not being disturbed by other system processes.

• The throughput_summary.txt provides the bottom line: did the system meet its performance target?

Part 9: Conclusion and Future Directions

This guide has provided a comprehensive, end-to-end framework for building, deploying, and testing an ultra-high-performance C++ trading system. We have journeyed from the bare metal of system requirements, through the intricate details of compiler optimizations and kernel tuning, and deep into the C++ code that forms the engine's core. We have demonstrated how to manage threads, memory, and CPU resources with the surgical precision required for low-latency applications.

The provided scripts and C++ code create a powerful foundation, capable of processing millions of messages per second with deterministic, microsecond-level latency. The key takeaways are the principles of mechanical sympathy and holistic system design. Performance is not a feature to be added later; it is the result of a thousand conscious decisions made at every level of the stack, from hardware selection and kernel parameters to cache-line alignment and lock-free algorithms.

What's Missing: The Reality of HFT

While this system is highly advanced, it is important to acknowledge what separates it from a top-tier institutional trading firm's infrastructure:

• Real Exchange Connectivity: The system simulates market data. A real system would interface directly with exchanges using protocols like FIX or proprietary binary protocols, often via specialized network cards.

• Kernel Bypass Networking: For the lowest possible network latency, firms use kernel-bypass technologies (like onload or DPDK) or even custom FPGA-based network stacks that deliver data directly from the wire to the application's memory without involving the OS. [11]

• Hardware Acceleration (FPGAs/ASICs): The most latency-critical parts of the logic (like order book maintenance or risk checks) are often offloaded from the CPU entirely and implemented in programmable hardware (FPGAs) or custom chips (ASICs), pushing latencies into the sub-nanosecond realm.

• Sophisticated Models and Infrastructure: The placeholder logic in our C++ code would be replaced by complex machine learning models for signal generation and parameter calibration, backed by petabytes of historical market data.

The "real secret sauce" is not one single algorithm, but the flawless, high-speed integration of all these components, operating at a massive scale with extreme reliability. The framework presented here, however, is the essential software blueprint upon which such world-class systems are built. It is a testament to the power of C++ and the Linux operating system when wielded with a deep understanding of the underlying hardware. The continuous cycle of measuring, analyzing, and optimizing is the true nature of the game in the world of high-frequency trading.

Learn more:

1. SIMD: Supercharging C++ with Hardware Optimization | by Byte Blog - Medium

2. Improving performance with SIMD intrinsics in three use cases - The Stack Overflow Blog

3. CPU pinning and NUMA boundaries | Design and Optimize a 5G Telco Cloud

4. CPU topologies — nova 31.1.0.dev357 documentation

5. AVX2 Optimization : r/cpp - Reddit

6. A Simple Lock-free Ring Buffer - Kmdreko's

7. Good thread safe ciruclar buffer implementations? : r/cpp - Reddit

8. CPU Pinning and NUMA Awareness in Vagrant | by Eren Cankut Uysal | Medium

9. Best Practices of systemd Service Optimization | by Eren Cankut Uysal | Medium

10. Boost Linux Performance Management with SystemD - Expert Tips and Techniques

11. When Nanoseconds Matter: Ultrafast Trading Systems in C++ - David Gross - CppCon 2024