
Worked on the ROCm/clr repository to address a critical memory visibility issue in HIP Graphs, focusing on ensuring host callbacks reliably observe GPU writes to CPU-accessible memory. Applied C++ synchronization primitives and system-scope acquire and release semantics to update the GraphHostNode, aligning AQL batch handling for correct data consistency. The solution covered scenarios involving managed and pinned memory, D2H copies, and both captured and uncaptured nodes. Regression and unit tests were added to validate host callback visibility, with all tests passing in Debug builds for gfx950. This work improved reliability and determinism for graph-driven workloads using HIP and ROCm.
Concise monthly summary for 2026-07 focused on business value and technical achievements for ROCm/clr. Key features delivered: - HIP Graphs memory visibility fix: Ensured host callbacks observe GPU writes to CPU-accessible memory by updating GraphHostNode to use a system-scope acquire marker and aligning single-packet AQL batch system-scope release. Commit 9722fac064fbe44a58c56ee0acd47355c13793e8 (ROCM-26916). Major bugs fixed: - Resolved memory visibility/data consistency issues between GPU producers and host consumers in HIP Graphs, covering scenarios with captured/un-captured nodes, D2H copies, and kernel writes to managed/pinned memory. - Ensured correctness of system-scope release for single-packet batches and updated related synchronization paths. Overall impact and accomplishments: - Improved correctness and reliability of HIP Graph workflows across CPU-GPU boundaries, reducing data-race risks and flakiness in graph execution. - Regression coverage added for host callback visibility, with 100% test pass in Debug builds for gfx950. - Documentation/test plan updates accompanying the changes to ensure future coverage of edge cases. Technologies/skills demonstrated: - C++ synchronization primitives, system-scope vs cache-scope semantics, and AQL batch handling in graph execution. - Regression testing, unit tests for host callbacks, and build/CI validation. - Cross-component impact analysis across HIP graphs, memory visibility guarantees, and host-device memory semantics. Business value: - For users relying on deterministic graph execution, this fix reduces nondeterministic host reads, improves stability of graph-driven workloads, and strengthens correctness guarantees for memory transfers between GPU producers and host consumers.
Concise monthly summary for 2026-07 focused on business value and technical achievements for ROCm/clr. Key features delivered: - HIP Graphs memory visibility fix: Ensured host callbacks observe GPU writes to CPU-accessible memory by updating GraphHostNode to use a system-scope acquire marker and aligning single-packet AQL batch system-scope release. Commit 9722fac064fbe44a58c56ee0acd47355c13793e8 (ROCM-26916). Major bugs fixed: - Resolved memory visibility/data consistency issues between GPU producers and host consumers in HIP Graphs, covering scenarios with captured/un-captured nodes, D2H copies, and kernel writes to managed/pinned memory. - Ensured correctness of system-scope release for single-packet batches and updated related synchronization paths. Overall impact and accomplishments: - Improved correctness and reliability of HIP Graph workflows across CPU-GPU boundaries, reducing data-race risks and flakiness in graph execution. - Regression coverage added for host callback visibility, with 100% test pass in Debug builds for gfx950. - Documentation/test plan updates accompanying the changes to ensure future coverage of edge cases. Technologies/skills demonstrated: - C++ synchronization primitives, system-scope vs cache-scope semantics, and AQL batch handling in graph execution. - Regression testing, unit tests for host callbacks, and build/CI validation. - Cross-component impact analysis across HIP graphs, memory visibility guarantees, and host-device memory semantics. Business value: - For users relying on deterministic graph execution, this fix reduces nondeterministic host reads, improves stability of graph-driven workloads, and strengthens correctness guarantees for memory transfers between GPU producers and host consumers.

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