The document discusses the performance comparisons of vector multiplication and element summation using different execution modes including sequential, OpenMP, and CUDA. It evaluates various storage types like float and bfloat16, as well as different launch configurations for CUDA. The analysis also includes in-place strategies and various techniques for optimizing vector element sums in CUDA.

1. Multiply with different modes (map)
Sequential OpenMP CUDA
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
float bfloat16
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
Sequential OpenMP CUDA (memcpy, in-place)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
sum-loop sum-reduce
one-loop atomic-add
block-loop template, next-pow2 launch one-reduce, next-pow2 launch
block-loop template, prev. pow2 launch one-reduce, prev-pow2 launch
grid-loop
1. Comparing various launch configs for CUDA based vector element sum (in-place).