June 2026 performance summary for facebookincubator/velox: TorchWave delivered major feature enhancements, reliability fixes, and GPU parity improvements that collectively raise throughput, correctness, and time-to-value for TorchWave workloads and the IG ROO pipeline. Key features delivered: - TorchWave core enhancements: performance instrumentation, a cost-model-based block allocator, zero-copy/memory-dependency and broadcast optimizations, and in-place/fused operation support. Documentation and user-facing reports were expanded to improve operability and observability. - Zero-copy parallel reference-frame loading: new TWREF format enabling mmap-based, zero-copy loading of large reference frames with per-tensor views, dramatically reducing startup time and memory copies for large workloads. - In-place ops and views: extended support for non-functionalized ATen graphs with storage aliasing, improved memory ordering, and enhanced timing/reporting for standalone ops; added fast paths for metadata-only host-side ops and fused view/slice paths. - IG ROO pipeline scalar support and parity: scalar handling and numeric parity with PyTorch eager execution on GPU, including new scalar operators and end-to-end GPU-accelerated ROO preproc. Major bugs fixed: - Execution engine reliability: forced alwaysSingleBlock ops to a single block in makeGrid to prevent cross-block deadlocks; resolved a compile warmup deadlock by relocating one-time NVRTC/system-header initialization to the main thread and removing a fork-based header init. - Timing and trace correctness: corrected timing attribution and gated trace outputs to avoid spurious data when tracing is disabled or limited by bits. Overall impact and accomplishments: - Substantial uplift in performance predictability and GPU utilization due to adaptive cost modeling and improved block sizing, along with faster startup for large models thanks to zero-copy loading. Reliability improvements reduce rare deadlocks and compilation stalls in large-scale deployments. Closer parity with PyTorch eager semantics improves correctness and confidence in production runs. Expanded test coverage and tooling set the stage for safer refactors and faster iteration. Technologies/skills demonstrated: - GPU kernel optimization, zero-copy memory mapping, and memory/broadcast-aware scheduling; dynamic cost modeling and latency balancing; in-place operation handling and view/slice optimizations; robust compile-time reliability fixes; extensive test tooling and CI coverage.

May 2026 Velox development wrap: delivered cross-repo GPU compute enhancements across IBM/velox and facebookincubator/velox. Implemented durable CUDA kernel caching with filesystem-based CUBIN caches and lifecycle hooks to persist compiled kernels across runs, significantly cutting NVRTC recompile costs. Launched TorchWave, a Pytorch FX Graph native executor for efficient GPU preprocessing, featuring fused kernels, adaptive thread-block allocation, and multiple GPU algorithm variants to improve performance on heterogeneous workloads. Rolled out comprehensive device-side fusion and execution improvements, including fused index/clone operations, enhanced device-side error reporting, a new multiBlockReturnBarrier mechanism, and refined elementwise code generation to support diverse tensor shapes and strides. Strengthened validation with targeted test suites for cubin/cache paths, index/gather/fused paths, and error injection. These changes jointly boost runtime performance, reduce cold-start kernel compile times, and improve GPU utilization for large-scale workloads.

In November 2024, delivered a Local Distributed Execution Runner for Velox, enabling local testing and development of distributed workloads. The work includes new classes and build-system updates to support the runner, along with fixes to CMake to resolve build-time errors and enable seamless local execution. These changes accelerate development velocity, improve test coverage for distributed scenarios, and align local workflow with distributed deployment expectations.