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90 lines
3.4 KiB
Plaintext
90 lines
3.4 KiB
Plaintext
GPU Device 0: "Hopper" with compute capability 9.0
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Driver version is: 12.0
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Running sample.
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================================
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Running virtual address reuse example.
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Sequential allocations & frees within a single graph enable CUDA to reuse virtual addresses.
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Check confirms that d_a and d_b share a virtual address.
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FOOTPRINT: 67108864 bytes
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Cleaning up example by trimming device memory.
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FOOTPRINT: 0 bytes
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================================
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Running physical memory reuse example.
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CUDA reuses the same physical memory for allocations from separate graphs when the allocation lifetimes don't overlap.
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Creating the graph execs does not reserve any physical memory.
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FOOTPRINT: 0 bytes
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The first graph launched reserves the memory it needs.
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FOOTPRINT: 67108864 bytes
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A subsequent launch of the same graph in the same stream reuses the same physical memory. Thus the memory footprint does not grow here.
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FOOTPRINT: 67108864 bytes
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Subsequent launches of other graphs in the same stream also reuse the physical memory. Thus the memory footprint does not grow here.
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01: FOOTPRINT: 67108864 bytes
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02: FOOTPRINT: 67108864 bytes
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03: FOOTPRINT: 67108864 bytes
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04: FOOTPRINT: 67108864 bytes
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05: FOOTPRINT: 67108864 bytes
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06: FOOTPRINT: 67108864 bytes
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07: FOOTPRINT: 67108864 bytes
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Check confirms all graphs use a different virtual address.
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Cleaning up example by trimming device memory.
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FOOTPRINT: 0 bytes
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================================
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Running simultaneous streams example.
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Graphs that can run concurrently need separate physical memory. In this example, each graph launched in a separate stream increases the total memory footprint.
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When launching a new graph, CUDA may reuse physical memory from a graph whose execution has already finished -- even if the new graph is being launched in a different stream from the completed graph. Therefore, a kernel node is added to the graphs to increase runtime.
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Initial footprint:
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FOOTPRINT: 0 bytes
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Each graph launch in a seperate stream grows the memory footprint:
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01: FOOTPRINT: 67108864 bytes
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02: FOOTPRINT: 134217728 bytes
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03: FOOTPRINT: 201326592 bytes
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04: FOOTPRINT: 268435456 bytes
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05: FOOTPRINT: 335544320 bytes
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06: FOOTPRINT: 402653184 bytes
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07: FOOTPRINT: 402653184 bytes
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Cleaning up example by trimming device memory.
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FOOTPRINT: 0 bytes
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================================
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Running unfreed streams example.
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CUDA cannot reuse phyiscal memory from graphs which do not free their allocations.
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Despite being launched in the same stream, each graph launch grows the memory footprint. Since the allocation is not freed, CUDA keeps the memory valid for use.
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00: FOOTPRINT: 67108864 bytes
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01: FOOTPRINT: 134217728 bytes
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02: FOOTPRINT: 201326592 bytes
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03: FOOTPRINT: 268435456 bytes
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04: FOOTPRINT: 335544320 bytes
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05: FOOTPRINT: 402653184 bytes
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06: FOOTPRINT: 469762048 bytes
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07: FOOTPRINT: 536870912 bytes
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Trimming does not impact the memory footprint since the un-freed allocations are still holding onto the memory.
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FOOTPRINT: 536870912 bytes
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Freeing the allocations does not shrink the footprint.
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FOOTPRINT: 536870912 bytes
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Since the allocations are now freed, trimming does reduce the footprint even when the graph execs are not yet destroyed.
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FOOTPRINT: 0 bytes
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Cleaning up example by trimming device memory.
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FOOTPRINT: 0 bytes
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================================
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Sample complete.
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