Optimizing RAM Performance: The Tail Slayer Technique

Every 3.9 microseconds, your RAM needs a break to recharge, causing a brief pause in data processing. This is due to the memory controller refreshing each cell to prevent data loss, a process that can delay operations by hundreds of nanoseconds. While this delay is negligible for most applications, it can be critical in high-frequency trading, where timing is everything.

Bob Dennard's invention of a memory system using a single transistor to store a bit revolutionized computing, but it came with the challenge of constant data leakage. This necessitated periodic refresh cycles that could stall operations. In modern CPUs, these stalls can burn thousands of cycles, but they're often unnoticed due to their brief duration compared to human perception.

To tackle this issue, I developed a benchmark program to make RAM stalls on DDR4 more apparent. By targeting a specific address and hammering it with millions of reads, I could observe the refresh stalls occurring every 7.82 microseconds. This periodicity raised the question: can we predict and avoid these stalls?

In high-frequency trading, where even a slight delay can cost millions, reducing latency spikes is crucial. Inspired by Google's approach to reducing web latency through hedged requests, I explored a similar concept for RAM reads. By duplicating data across different memory channels, I aimed to dodge refresh stalls by reading from the channel that wasn't busy.

The challenge was predicting when and where these stalls would occur. Modern CPUs complicate this with out-of-order execution and memory address scrambling, making it difficult to ensure data ends up on separate channels. However, by using huge pages and understanding the memory controller's behavior, I could map out which bits influenced channel placement.

Through extensive testing on various systems, including AMD and Intel CPUs, I discovered that offsetting data by 256 bytes could place it on different channels, effectively avoiding simultaneous stalls. This strategy, dubbed 'Tail Slayer,' significantly reduced tail latency, especially in systems with multiple memory channels.

Despite the complexity of modern memory systems and the lack of public documentation on address hashing, Tail Slayer proved effective across different architectures. By leveraging multicore processors and understanding memory controller behavior, we can achieve near-deterministic latency, crucial for applications like high-frequency trading.

This technique, although requiring additional resources like extra cores and memory duplication, offers a promising solution to the age-old problem of RAM refresh stalls. As I continue to refine and test Tail Slayer, I invite others to explore its potential applications in real-world scenarios.