Browse past weeks of engineering reads.
Facebook Reels needed a way to enhance social discovery by surfacing content that friends have interacted with, requiring real-time computation of relationship strength and ranking of friend-engaged content at massive scale.
Meta needed to handle massive-scale media processing (encoding, transcoding, filtering) across its family of apps, requiring efficient orchestration of complex audio/video pipelines using FFmpeg at an unprecedented scale.
Meta's large-scale infrastructure relies on jemalloc for memory allocation, but the codebase had accumulated maintenance burden and needed modernization to keep pace with evolving hardware and workload demands.
GPU-to-GPU communication performance on AMD platforms was insufficient for Meta's evolving AI model training workloads, and the standard RCCL library didn't meet the performance and flexibility requirements of their internal workloads.
Connecting thousands of GPUs across multiple data centers and regions for gigawatt-scale AI training clusters requires seamlessly bridging different network fabrics, which creates massive networking and interconnect challenges.