December 2025 monthly summary for DataDog/datadog-agent focusing on GPU device discovery and health monitoring.

November 2025 (DataDog/datadog-agent) focused on expanding GPU observability by introducing a new device model architecture tag for GPU metrics. This enables segmentation by architecture (e.g., Blackwell, Hopper) across GPU workloads, improving dashboards, alerts, and capacity planning. Implemented via the tagger/gpu change in EBPF instrumentation; commit 85c6f0a5872eb97c5e8cbd59bd4fc5f66f5442c1 with message [EBPF-888] update(tagger/gpu): add gpu architecture tag (#42928). No major bugs fixed documented this month; feature delivered to extend metrics tagging. This contributes to business value by enabling more precise GPU usage analytics and faster issue isolation, and demonstrates skills in observability, EBPF tagging, and Go code changes.

October 2025: Delivered key GPU monitoring and observability enhancements across DataDog/datadog-agent and DataDog/integrations-core. The work improves end-to-end visibility, reliability, and incident response for GPU workloads through EBPF instrumentation, dynamic device management, and metadata-driven metrics. Key outcomes: - Expanded GPU metrics surface with new XID error metric and NSPID tagging; improved collection resilience and dynamic device handling in the agent. - Strengthened test stability for GPU EBPF code with targeted fixes to flaky tests. - Implemented lazy initialization and registration retry for GPU device events collector, reducing onboarding and runtime errors. - Added device UUID logging on failed collector creation to aid diagnostics. - Extended GPU observability in integrations-core with the total XID errors metric and accompanying metadata/docs to improve reliability tracking.

Monthly performance summary for 2025-09 focusing on DataDog/datadog-agent work: delivered improvements in memory footprint reporting for eBPF maps, enhanced GPU performance analysis through CUDA synchronization tracing, and stabilized GPU tests to improve CI reliability. These efforts improve resource visibility, enable more accurate performance tuning, and reduce flaky tests in GPU-heavy workloads.