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import numpy as np
import pytest
from numba import cuda
from pyecsca.sca import StackedTraces, GPUTraceManager, CombinedTrace, CPUTraceManager
TPB = 128
TRACE_COUNT = 2**10
TRACE_LEN = 2**10
RTOL = 1e-5
ATOL = 1e-5
CHUNK_SIZE = 2**5
STREAM_COUNT = 4
@pytest.fixture()
def samples():
np.random.seed(0x1234)
return np.random.rand(TRACE_COUNT, TRACE_LEN)
class Base:
@pytest.fixture()
def manager(self, samples):
raise NotImplementedError("Subclasses should implement this")
def test_average(self, samples, manager):
avg_trace = manager.average()
avg_cmp: np.ndarray = np.average(samples, 0)
assert isinstance(avg_trace, CombinedTrace)
assert avg_trace.samples.shape == avg_cmp.shape
assert all(np.isclose(avg_trace.samples, avg_cmp))
def test_standard_deviation(self, samples, manager):
std_trace = manager.standard_deviation()
std_cmp: np.ndarray = np.std(samples, 0)
assert isinstance(std_trace, CombinedTrace)
assert std_trace.samples.shape == std_cmp.shape
assert all(np.isclose(std_trace.samples, std_cmp))
def test_variance(self, samples, manager):
var_trace = manager.variance()
var_cmp: np.ndarray = np.var(samples, 0)
assert isinstance(var_trace, CombinedTrace)
assert var_trace.samples.shape == var_cmp.shape
assert all(np.isclose(var_trace.samples, var_cmp))
def test_average_and_variance(self, samples, manager):
avg_trace, var_trace = manager.average_and_variance()
avg_cmp: np.ndarray = np.average(samples, 0)
var_cmp: np.ndarray = np.var(samples, 0)
assert isinstance(avg_trace, CombinedTrace)
assert isinstance(var_trace, CombinedTrace)
assert avg_trace.samples.shape == avg_cmp.shape
assert var_trace.samples.shape == var_cmp.shape
assert all(np.isclose(avg_trace.samples, avg_cmp))
assert all(np.isclose(var_trace.samples, var_cmp))
def test_pearson_coef(self, samples, manager):
np.random.seed(0x1234)
intermediate_values = np.random.rand(TRACE_COUNT)
corr_gpu = manager.pearson_corr(intermediate_values)
corr_cmp = np.corrcoef(samples, intermediate_values, rowvar=False)[-1, :-1]
assert isinstance(corr_gpu, CombinedTrace)
assert corr_gpu.samples.shape == corr_cmp.shape
assert all(np.isclose(corr_gpu.samples, corr_cmp, rtol=RTOL, atol=ATOL))
def test_pearson_coef_invalid(self, samples, manager):
intermediate_values = np.ones(TRACE_COUNT)
with pytest.raises(ValueError):
manager.pearson_corr(intermediate_values)
class TestGPUNonChunked(Base):
@pytest.fixture()
def manager(self, samples):
if not cuda.is_available():
pytest.skip("CUDA not available")
return GPUTraceManager(StackedTraces(samples), TPB)
class TestGPUChunked(Base):
@pytest.fixture()
def manager(self, samples):
if not cuda.is_available():
pytest.skip("CUDA not available")
return GPUTraceManager(
StackedTraces(samples),
TPB,
chunk_size=CHUNK_SIZE,
stream_count=STREAM_COUNT,
)
class TestCPU(Base):
@pytest.fixture()
def manager(self, samples):
return CPUTraceManager(StackedTraces(samples))
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