aclrtLaunchKernelAttrValue

typedef union aclrtLaunchKernelAttrValue {
    uint8_t schemMode;
    uint32_t dynUBufSize;
    aclrtEngineType engineType; 
    uint32_t blockDimOffset; 
    uint8_t isBlockTaskPrefetch; 
    uint8_t isDataDump; 
    uint16_t timeout;
    aclrtTimeoutUs timeoutUs;
    uint32_t rsv[4];
} aclrtLaunchKernelAttrValue;

Member	Description
schemMode	Scheduling mode. The options are as follows: 0: common scheduling mode. If there is an idle core, the operator execution is started. For example, if numBlocks is set to 8, the operator kernel function will be executed on eight cores. If the common scheduling mode is specified, the operator execution is started as long as one core is idle. 1: batch scheduling mode. The operator execution is started only when all required cores are idle. For example, if numBlocks is set to 8, the operator kernel function will be executed on eight cores. If the batch scheduling mode is specified, the operator execution is started only when all the eight cores are idle.
dynUBufSize	Size of the dynamic Unified Buffer (UB) required for executing the Single Instruction Multiple Threads (SIMT) operator, in bytes. For pure SIMD operators, this parameter can only be set to 0. Only the Atlas 350 Accelerator Card supports this parameter. This parameter is not supported for other product models currently, and its setting does not take effect.
engineType	Operator execution engine. For details about the type definition, see aclrtEngineType. Only the Atlas inference product supports this parameter. This parameter is not supported for the following product models, and its setting does not take effect: 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 training product
blockDimOffset	numBlocks offset. numBlocks ≤ Number of AI Cores: Computation on the Vector Core is not required. In this case, set engineType to ACL_RT_ENGINE_TYPE_AIC (indicating computation on the AI Core) and set blockDimOffset to 0. numBlocks > AI Core cores: Deliver a task in a stream, set engineType to ACL_RT_ENGINE_TYPE_AIC (indicating computation on the AI Core), and set blockDimOffset to 0. Deliver a task in another stream, set engineType to ACL_RT_ENGINE_TYPE_AIV (indicating computation on the Vector Core), and set blockDimOffset to aicorenumBlocks. The formula for calculating aicorenumBlocks is as follows: If numBlocks ≤ Number of AI Cores + Number of Vector Cores, then aicorenumBlocks = Number of AI Cores. Otherwise, aicorenumBlocks = Roundup (numBlocks x Number of AI Cores)/(Number of AI Cores + Number of Vector Cores). Only the Atlas inference product supports this parameter. This parameter is not supported for the following product models, and its setting does not take effect: 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 training product
isBlockTaskPrefetch	Whether to block hardware from prefetching the information of the current task when the task is delivered. The options are as follows: 0: Do not block. 1: Block.
isDataDump	Whether to enable dump. The options are as follows: 0: disabled 1: enabled
timeout	Timeout interval for the task scheduler to wait for task execution. This parameter applies only to the scenario where the AI CPU or AI Core operator is executed. The options are as follows: 0: Wait forever. > 0: Specific timeout, in seconds.
timeoutUs	Timeout interval for the task scheduler to wait for task execution, in microseconds. For details about the type definition, see aclrtTimeoutUs. If both timeoutLow and timeoutHigh in the aclrtTimeoutUs structure are set to 0, it indicates forever waiting. For the same launch kernel task, timeoutUs and timeout cannot be configured at the same time. Otherwise, an error is returned.
rsv	Reserved. The value is fixed at 0.

Parent topic: Data Types and Operation APIs