Measure the memory bandwidth using the following streaming kernels with corresponding data access pattern (Notation: S - store, L - load, WA - write allocate). All variables are vectors, s is a scalar:

init (S1, WA): Initilize an array: `a = s`. Store only.
sum (L1): Vector reduction: `s += a`. Load only.
copy (L1, S1, WA): Classic memcopy: `a = b`.
update (L1, S1): Update vector: `a = a * scalar`. Also load + store but without write allocate.
triad (L2, S1, WA): Stream triad: `a = b + c * scalar`.
daxpy (L2, S1): Daxpy: `a = a + b * scalar`.
striad (L3, S1, WA): Schoenauer triad: `a = b + c * d`.
sdaxpy (L3, S1): Schoenauer triad without write allocate: `a = a + b * c`.

Keyword arguments:

• N (default: 120_000_000): length of vectors
• nthreads (default: Threads.nthreads()): number of Julia threads to use
• niter (default: 10): # of times we repeat the measurement
• alignment (default: 64): array alignment
• verbose (default: false): print result table + thread information etc.
• write_allocate (default: false): include write allocate compensation factors

Uses bwbench to measure the memory bandwidth for an increasing number of threads (1:max_nthreads). Returns a matrix whose rows correspond to the number of threads and different columns hold the bandwidth results for each kernel.

Similar to bwscaling but measures the memory bandwidth scaling within and across memory domains. Returns a DataFrame in which each row contains the kernel name, the number of threads per memory domain, the number of domains considered, and the measured memory bandwidth (in MB/s).

Keyword arguments

• domains: memory domains to consider (logical indices, i.e. starting at 1)
• max_nthreads: maximal number of threads per memory domain to consider