ai_vector_core_utilization (AI Vector Core Instruction Proportion)

The AI Vector Core instruction proportion data does not contain the timeline information. The summary information is summarized in the ai_vector_core_utilization_*.csv file.

Applicability

Product	Supported
Atlas 350 Accelerator Card	√
Atlas A3 training product/Atlas A3 inference product	√
Atlas A2 training product/Atlas A2 inference product	√
Atlas 200I/500 A2 inference product	√
Atlas inference product	x
Atlas training product	x

ai_vector_core_utilization_*.csv File

The following shows the file content format.

Figure 1 ai_vector_core_utilization_*.csv

**Table 1** Field description
Field	Description
vec_ratio	Ratio of cycles taken to execute Vector instructions to the total cycles. The Atlas 200I/500 A2 inference product does not support this field, and the default value N/A is used.
mac_ratio	Ratio of cycles taken to execute Cube instructions (fp16 and s16) to the total cycles.
scalar_ratio	Ratio of cycles taken to execute Scalar instructions to the total cycles.
mte1_ratio	Ratio of cycles taken to execute MTE1 instructions (L1-to-L0A/L0B transfer) to the total cycles.
mte2_ratio	Ratio of cycles taken to execute MTE2 instructions (DDR-to-AI Core transfer) to the total cycles. (Atlas 200I/500 A2 inference product)
mte2_ratio	Ratio of cycles taken to execute MTE2 instructions (on-chip memory to AI Core movement) to the total cycles. (Atlas A2 training product/Atlas A2 inference product) (Atlas A3 training product/Atlas A3 inference product)
mte3_ratio	Ratio of cycles taken to execute MTE3 instructions (AI Core-to-DDR transfer) to the total cycles. (Atlas 200I/500 A2 inference product)
mte3_ratio	Ratio of cycles taken to execute MTE3 instructions (AI Core to on-chip memory movement) to the total cycles. (Atlas A2 training product/Atlas A2 inference product) (Atlas A3 training product/Atlas A3 inference product)
icache_miss_rate	iCache miss rate, that is, L1 cache that does not hit instructions. The smaller the value, the better.
memory_bound	AI Core memory bound, calculated as: mte2_ratio/max(mac_ratio, vec_ratio). If the value is less than 1, no memory bound exists. If the value is greater than 1, a memory bound exists. A greater value indicates a severer bound.
See ai_core_utilization (AI Core Instruction Proportion) for more details. The AI Vector Core metric PipeUtilization in sample-based mode is taken as an example.

Parent topic: Profile Data File References