def run_memory(self):
return memory.no_simd_int_cum_sum_write(
num_elements=self.mnist_np_array.
size, # Sparse is assumed to be a list of (pos, val)
num_input_arrays=1,
num_threads=4,
) * MICR_TO_MS
def run_memory(self, encoding='rle'):
if encoding == 'rle':
num_elements = 2 * self.megatensor.rle_length()
elif encoding == 'mixed':
num_elements = sum(e.size for e in self.numpy_chunk_list)
else:
raise RuntimeError("BAD")
return memory.no_simd_int_cum_sum_write(
num_elements=num_elements,
num_input_arrays=1,
num_threads=4,
) * MICR_TO_MS
def run_memory(self, encoding='sparse'):
if encoding == 'sparse':
num_elements = self.num_non_zero * 2 * self.run_length
elif encoding == 'dense':
num_elements = self.array_length
elif encoding == 'rle':
num_elements = self.num_non_zero * 2
return memory.no_simd_int_cum_sum_write(
num_elements=num_elements,
num_input_arrays=self.num_inputs,
num_threads=4,
) * MICR_TO_MS
def run_memory(self):
return memory.no_simd_int_cum_sum_write(
num_elements=self.array_length,
num_input_arrays=1,
num_threads=4,
) * MICR_TO_MS