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Utilizes the HIP Memory Manager (hipMM) for advanced GPU memory pooling, efficient allocation, and data movement.
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Based on SOC occupation classification
Placeholder skill description.
Verifies whether a requested ROCm-DS workflow is officially supported, source-build feasible, or experimental on the target system.
Migrates pandas-like workflows to hipDF and cudf.pandas style acceleration, auditing for unsupported features.
Leverages GPU acceleration to process and analyze complex graph structures using hipGRAPH. Note: hipGRAPH is early access.
Utilizes hipRAFT for foundational, reusable GPU-accelerated primitives like clustering, dimensionality reduction, and statistical operations.
Selects, builds, benchmarks, and validates hipVS ANN indexes and query paths for ROCm-DS workloads.
| name | hipmm-memory-ops |
| description | Utilizes the HIP Memory Manager (hipMM) for advanced GPU memory pooling, efficient allocation, and data movement. |
| tools | ["bash","python"] |
| inputs | ["memory_profile","pipeline_components"] |
| outputs | ["memory_optimization_report"] |
| tags | ["rocm","rocm-ds","hipmm","memory"] |
Provide advanced GPU memory management utilities to support various libraries that form part of ROCm-DS, mitigating OOM errors and fragmentation.
