Viewing Profiling Results Using Vitis Analyzer - 2023.1 English

AI Engine Tools and Flows User Guide (UG1076)

Document ID
UG1076
Release Date
2023-06-23
Version
2023.1 English

To launch the vitis_analyzer to view the profiling information in the XRT flow, use the following command.

vitis_analyzer xrt.run_summary

To launch the vitis_analyzer to view the profiling information in the XSDB flow, use the following command.

vitis_analyzer aie_trace_profile.run_summary

Example of heat_map Core Metrics and conflicts Memory Metrics

The following image shows the design's active time, stall time, cumulative instruction count, and vector_instruction_count as part of heat_map metric and memory conflict time, as well as cumulative memory error time of conflicts metrics for ten tiles of an example design.

Figure 1. Example of heat_map and conflicts Metrics

Consider the AI Engine located in (15,0). During the active utilization time (5.120 ms) it performs 5120000 vector instructions which represents 87% of the active time. This is an excellent performance that indicates a well optimized core.

Example of stalls Core Metrics and dma_locks Memory Metrics

The following image shows the design's memory stall time, stream stall time, cascade stall time, and lock stall time as part of stalls metrics and cumulative DMA activity time, as well as cumulative DMA locks count of dma_locks metrics for ten tiles of an example design.

Figure 2. Example of stalls and dma_locks Metrics

On the core (24,2), the DMA has been active for 70.645 ms (77.8 millions instructions), but has been stalled 298 times.

Example of execution Core Metrics and conflicts Memory Metrics

The following image shows the design's cumulative instruction count, vector instruction count, load instruction count, and store instruction count as part of execution metrics and memory conflict time, as well as cumulative memory error time of conflicts metrics for ten tiles of an example design.

Figure 3. Example of execution and conflicts Metrics

Although they are minor, core (15,1) suffers from some memory conflicts that must be identified. The occurrence being very small might be due to some DMA or some other kernel access interference.

Example of read_throughputs and write_throughputs AI Engine Metrics and dma_stalls_s2mm and dma_stalls_mm2s AI Engine Memory Metrics

The following image shows the design's stream and cascade read and write instruction count as part of read_throughputs and write_throughputs metrics and s2mm and mm2s channel0 and channel1 stalls time of dma_stalls_s2mm and dma_stalls_mm2smetrics for ten tiles of an example design.

Figure 4. Data Table for read and write throughputs of the AI Engines and stalls of all mm2s and s2mm Channels of the AI Engine Memories