Project comparisons#

Here is a list of NumPy APIs and corresponding cuNumeric implementations.

A dot in the cunumeric column denotes that cuNumeric implementation is not provided yet. We welcome contributions for these functions.

NumPy vs cuNumeric APIs#

Module-Level#

NumPy	cunumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.arange`	`cunumeric.arange`	✓	✓
`numpy.array`	`cunumeric.array`	✓	✓
`numpy.asanyarray`
`numpy.asarray`	`cunumeric.asarray`	✓	✓
`numpy.asarray_chkfinite`
`numpy.ascontiguousarray`
`numpy.asfortranarray`
`numpy.asmatrix`
`numpy.bartlett`	`cunumeric.bartlett`	✓	✓
`numpy.base_repr`
`numpy.binary_repr`
`numpy.blackman`	`cunumeric.blackman`	✓	✓
`numpy.bmat`
`numpy.bool`
`numpy.bool_`
`numpy.broadcast`
`numpy.broadcast_shapes`	`cunumeric.broadcast_shapes`	✓	✓
`numpy.busdaycalendar`
`numpy.byte`
`numpy.bytes_`
`numpy.cdouble`
`numpy.character`
`numpy.clongdouble`
`numpy.complex128`
`numpy.complex256`
`numpy.complex64`
`numpy.complexfloating`
`numpy.csingle`
`numpy.datetime64`
`numpy.datetime_data`
`numpy.diag_indices`	`cunumeric.diag_indices`	✓	✓
`numpy.double`
`numpy.dtype`
`numpy.empty`	`cunumeric.empty`	✓	✓
`numpy.errstate`
`numpy.eye`	`cunumeric.eye`	✓	✓
`numpy.finfo`
`numpy.flatiter`
`numpy.flexible`
`numpy.float128`
`numpy.float16`
`numpy.float32`
`numpy.float64`
`numpy.floating`
`numpy.format_float_positional`
`numpy.format_float_scientific`
`numpy.from_dlpack`
`numpy.frombuffer`
`numpy.fromfile`
`numpy.fromfunction`
`numpy.fromiter`
`numpy.frompyfunc`
`numpy.fromregex`
`numpy.fromstring`
`numpy.full`	`cunumeric.full`	✓	✓
`numpy.generic`
`numpy.genfromtxt`
`numpy.get_include`
`numpy.get_printoptions`
`numpy.getbufsize`
`numpy.geterr`
`numpy.geterrcall`
`numpy.half`
`numpy.hamming`	`cunumeric.hamming`	✓	✓
`numpy.hanning`	`cunumeric.hanning`	✓	✓
`numpy.identity`	`cunumeric.identity`	✓	✓
`numpy.iinfo`
`numpy.indices`	`cunumeric.indices`	✓	✓
`numpy.inexact`
`numpy.info`
`numpy.int16`
`numpy.int32`
`numpy.int64`
`numpy.int8`
`numpy.int_`
`numpy.intc`
`numpy.integer`
`numpy.intp`
`numpy.isdtype`
`numpy.isfortran`
`numpy.isscalar`	`cunumeric.isscalar`	✓	✓
`numpy.issubdtype`
`numpy.kaiser`	`cunumeric.kaiser`	✓	✓
`numpy.load`	`cunumeric.load`	❌	❌
`numpy.loadtxt`
`numpy.long`
`numpy.longdouble`
`numpy.longlong`
`numpy.mask_indices`	`cunumeric.mask_indices`	✓	✓
`numpy.matrix`
`numpy.memmap`
`numpy.mintypecode`
`numpy.ndarray()`
`numpy.ndenumerate`
`numpy.ndindex`
`numpy.nditer`
`numpy.nested_iters`
`numpy.number`
`numpy.object_`
`numpy.ones`	`cunumeric.ones`	✓	✓
`numpy.poly1d`
`numpy.printoptions`
`numpy.promote_types`
`numpy.recarray`
`numpy.record`
`numpy.require`
`numpy.row_stack`	`cunumeric.row_stack`	✓	✓
`numpy.set_printoptions`
`numpy.setbufsize`
`numpy.seterr`
`numpy.seterrcall`
`numpy.short`
`numpy.show_config`
`numpy.show_runtime`
`numpy.signedinteger`
`numpy.single`
`numpy.str_`
`numpy.timedelta64`
`numpy.trapz`
`numpy.tri`	`cunumeric.tri`	✓	✓
`numpy.tril_indices`	`cunumeric.tril_indices`	✓	✓
`numpy.triu_indices`	`cunumeric.triu_indices`	✓	✓
`numpy.typename`
`numpy.ubyte`
`numpy.ufunc`
`numpy.uint`
`numpy.uint16`
`numpy.uint32`
`numpy.uint64`
`numpy.uint8`
`numpy.uintc`
`numpy.uintp`
`numpy.ulong`
`numpy.ulonglong`
`numpy.unsignedinteger`
`numpy.ushort`
`numpy.vectorize`
`numpy.void`
`numpy.zeros`	`cunumeric.zeros`	✓	✓

Summary

Number of NumPy functions: 139

Number of functions covered by cunumeric: 24

Ufuncs#

NumPy	cunumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.absolute`	`cunumeric.absolute`	✓	✓
`numpy.acos`
`numpy.acosh`
`numpy.add`	`cunumeric.add`	✓	✓
`numpy.arccos`	`cunumeric.arccos`	✓	✓
`numpy.arccosh`	`cunumeric.arccosh`	✓	✓
`numpy.arcsin`	`cunumeric.arcsin`	✓	✓
`numpy.arcsinh`	`cunumeric.arcsinh`	✓	✓
`numpy.arctan`	`cunumeric.arctan`	✓	✓
`numpy.arctan2`	`cunumeric.arctan2`	✓	✓
`numpy.arctanh`	`cunumeric.arctanh`	✓	✓
`numpy.asin`
`numpy.asinh`
`numpy.atan`
`numpy.atan2`
`numpy.atanh`
`numpy.bitwise_and`	`cunumeric.bitwise_and`	✓	✓
`numpy.bitwise_count`
`numpy.bitwise_invert`
`numpy.bitwise_left_shift`
`numpy.bitwise_or`	`cunumeric.bitwise_or`	✓	✓
`numpy.bitwise_right_shift`
`numpy.bitwise_xor`	`cunumeric.bitwise_xor`	✓	✓
`numpy.cbrt`	`cunumeric.cbrt`	✓	✓
`numpy.ceil`	`cunumeric.ceil`	✓	✓
`numpy.conj`	`cunumeric.conj`	✓	✓
`numpy.conjugate`	`cunumeric.conjugate`	✓	✓
`numpy.copysign`	`cunumeric.copysign`	✓	✓
`numpy.cos`	`cunumeric.cos`	✓	✓
`numpy.cosh`	`cunumeric.cosh`	✓	✓
`numpy.deg2rad`	`cunumeric.deg2rad`	✓	✓
`numpy.degrees`	`cunumeric.degrees`	✓	✓
`numpy.divide`	`cunumeric.divide`	✓	✓
`numpy.divmod`
`numpy.equal`	`cunumeric.equal`	✓	✓
`numpy.exp`	`cunumeric.exp`	✓	✓
`numpy.exp2`	`cunumeric.exp2`	✓	✓
`numpy.expm1`	`cunumeric.expm1`	✓	✓
`numpy.fabs`	`cunumeric.fabs`	✓	✓
`numpy.float_power`	`cunumeric.float_power`	✓	✓
`numpy.floor`	`cunumeric.floor`	✓	✓
`numpy.floor_divide`	`cunumeric.floor_divide`	✓	✓
`numpy.fmax`	`cunumeric.fmax`	✓	✓
`numpy.fmin`	`cunumeric.fmin`	✓	✓
`numpy.fmod`	`cunumeric.fmod`	✓	✓
`numpy.frexp`	`cunumeric.frexp`	✓	✓
`numpy.gcd`	`cunumeric.gcd`	✓	✓
`numpy.greater`	`cunumeric.greater`	✓	✓
`numpy.greater_equal`	`cunumeric.greater_equal`	✓	✓
`numpy.heaviside`
`numpy.hypot`	`cunumeric.hypot`	✓	✓
`numpy.invert`	`cunumeric.invert`	✓	✓
`numpy.isfinite`	`cunumeric.isfinite`	✓	✓
`numpy.isinf`	`cunumeric.isinf`	✓	✓
`numpy.isnan`	`cunumeric.isnan`	✓	✓
`numpy.isnat`
`numpy.lcm`	`cunumeric.lcm`	✓	✓
`numpy.ldexp`	`cunumeric.ldexp`	✓	✓
`numpy.left_shift`	`cunumeric.left_shift`	✓	✓
`numpy.less`	`cunumeric.less`	✓	✓
`numpy.less_equal`	`cunumeric.less_equal`	✓	✓
`numpy.log`	`cunumeric.log`	✓	✓
`numpy.log10`	`cunumeric.log10`	✓	✓
`numpy.log1p`	`cunumeric.log1p`	✓	✓
`numpy.log2`	`cunumeric.log2`	✓	✓
`numpy.logaddexp`	`cunumeric.logaddexp`	✓	✓
`numpy.logaddexp2`	`cunumeric.logaddexp2`	✓	✓
`numpy.logical_and`	`cunumeric.logical_and`	✓	✓
`numpy.logical_not`	`cunumeric.logical_not`	✓	✓
`numpy.logical_or`	`cunumeric.logical_or`	✓	✓
`numpy.logical_xor`	`cunumeric.logical_xor`	✓	✓
`numpy.matmul`	`cunumeric.matmul`	✓	✓
`numpy.maximum`	`cunumeric.maximum`	✓	✓
`numpy.minimum`	`cunumeric.minimum`	✓	✓
`numpy.mod`	`cunumeric.mod`	✓	✓
`numpy.modf`	`cunumeric.modf`	✓	✓
`numpy.multiply`	`cunumeric.multiply`	✓	✓
`numpy.negative`	`cunumeric.negative`	✓	✓
`numpy.nextafter`	`cunumeric.nextafter`	✓	✓
`numpy.not_equal`	`cunumeric.not_equal`	✓	✓
`numpy.positive`	`cunumeric.positive`	✓	✓
`numpy.pow`
`numpy.power`	`cunumeric.power`	✓	✓
`numpy.rad2deg`	`cunumeric.rad2deg`	✓	✓
`numpy.radians`	`cunumeric.radians`	✓	✓
`numpy.reciprocal`	`cunumeric.reciprocal`	✓	✓
`numpy.remainder`	`cunumeric.remainder`	✓	✓
`numpy.right_shift`	`cunumeric.right_shift`	✓	✓
`numpy.rint`	`cunumeric.rint`	✓	✓
`numpy.sign`	`cunumeric.sign`	✓	✓
`numpy.signbit`	`cunumeric.signbit`	✓	✓
`numpy.sin`	`cunumeric.sin`	✓	✓
`numpy.sinh`	`cunumeric.sinh`	✓	✓
`numpy.spacing`
`numpy.sqrt`	`cunumeric.sqrt`	✓	✓
`numpy.square`	`cunumeric.square`	✓	✓
`numpy.subtract`	`cunumeric.subtract`	✓	✓
`numpy.tan`	`cunumeric.tan`	✓	✓
`numpy.tanh`	`cunumeric.tanh`	✓	✓
`numpy.true_divide`	`cunumeric.true_divide`	✓	✓
`numpy.trunc`	`cunumeric.trunc`	✓	✓
`numpy.vecdot`

Summary

Number of NumPy functions: 102

Number of functions covered by cunumeric: 85

Multi-Dimensional Array#

NumPy	cunumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.ndarray.all()`	`cunumeric.ndarray.all()`	✓	✓
`numpy.ndarray.any()`	`cunumeric.ndarray.any()`	✓	✓
`numpy.ndarray.argmax()`	`cunumeric.ndarray.argmax()`	✓	✓
`numpy.ndarray.argmin()`	`cunumeric.ndarray.argmin()`	✓	✓
`numpy.ndarray.argpartition()`	`cunumeric.ndarray.argpartition()`	✓	✓
`numpy.ndarray.argsort()`	`cunumeric.ndarray.argsort()`	✓	✓
`numpy.ndarray.astype()`	`cunumeric.ndarray.astype()`	✓	✓
`numpy.ndarray.byteswap()`
`numpy.ndarray.choose()`	`cunumeric.ndarray.choose()`	✓	✓
`numpy.ndarray.clip()`	`cunumeric.ndarray.clip()`	✓	✓
`numpy.ndarray.compress()`	`cunumeric.ndarray.compress()`	✓	✓
`numpy.ndarray.conj()`	`cunumeric.ndarray.conj()`	✓	✓
`numpy.ndarray.conjugate()`	`cunumeric.ndarray.conjugate()`	✓	✓
`numpy.ndarray.copy()`	`cunumeric.ndarray.copy()`	✓	✓
`numpy.ndarray.cumprod()`	`cunumeric.ndarray.cumprod()`	❌	❌
`numpy.ndarray.cumsum()`	`cunumeric.ndarray.cumsum()`	❌	❌
`numpy.ndarray.diagonal()`	`cunumeric.ndarray.diagonal()`	✓	✓
`numpy.ndarray.dot()`	`cunumeric.ndarray.dot()`	✓	✓
`numpy.ndarray.dump()`	`cunumeric.ndarray.dump()`	❌	❌
`numpy.ndarray.dumps()`	`cunumeric.ndarray.dumps()`	✓	✓
`numpy.ndarray.fill()`	`cunumeric.ndarray.fill()`	✓	✓
`numpy.ndarray.flatten()`	`cunumeric.ndarray.flatten()`	✓	✓
`numpy.ndarray.getfield()`	`cunumeric.ndarray.getfield()`	❌	❌
`numpy.ndarray.item()`	`cunumeric.ndarray.item()`	✓	✓
`numpy.ndarray.mean()`	`cunumeric.ndarray.mean()`	✓	✓
`numpy.ndarray.nonzero()`	`cunumeric.ndarray.nonzero()`	✓	✓
`numpy.ndarray.partition()`	`cunumeric.ndarray.partition()`	✓	✓
`numpy.ndarray.prod()`	`cunumeric.ndarray.prod()`	✓	✓
`numpy.ndarray.put()`	`cunumeric.ndarray.put()`	✓	✓
`numpy.ndarray.ravel()`	`cunumeric.ndarray.ravel()`	✓	✓
`numpy.ndarray.repeat()`
`numpy.ndarray.reshape()`	`cunumeric.ndarray.reshape()`	✓	✓
`numpy.ndarray.resize()`
`numpy.ndarray.searchsorted()`	`cunumeric.ndarray.searchsorted()`	✓	✓
`numpy.ndarray.setfield()`	`cunumeric.ndarray.setfield()`	❌	❌
`numpy.ndarray.setflags()`	`cunumeric.ndarray.setflags()`	✓	✓
`numpy.ndarray.sort()`	`cunumeric.ndarray.sort()`	✓	✓
`numpy.ndarray.squeeze()`	`cunumeric.ndarray.squeeze()`	✓	✓
`numpy.ndarray.std()`
`numpy.ndarray.sum()`	`cunumeric.ndarray.sum()`	✓	✓
`numpy.ndarray.swapaxes()`	`cunumeric.ndarray.swapaxes()`	✓	✓
`numpy.ndarray.take()`	`cunumeric.ndarray.take()`	✓	✓
`numpy.ndarray.to_device()`
`numpy.ndarray.tobytes()`	`cunumeric.ndarray.tobytes()`	✓	✓
`numpy.ndarray.tofile()`	`cunumeric.ndarray.tofile()`	❌	❌
`numpy.ndarray.tolist()`	`cunumeric.ndarray.tolist()`	✓	✓
`numpy.ndarray.tostring()`	`cunumeric.ndarray.tostring()`	✓	✓
`numpy.ndarray.trace()`	`cunumeric.ndarray.trace()`	✓	✓
`numpy.ndarray.transpose()`	`cunumeric.ndarray.transpose()`	✓	✓
`numpy.ndarray.var()`	`cunumeric.ndarray.var()`	✓	✓
`numpy.ndarray.view()`	`cunumeric.ndarray.view()`	✓	✓

Summary

Number of NumPy functions: 51

Number of functions covered by cunumeric: 46

Linear Algebra#

NumPy	cunumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.linalg.LinAlgError`

Summary

Number of NumPy functions: 1

Number of functions covered by cunumeric: 0

Discrete Fourier Transform#

NumPy	cunumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.fft.fftfreq`
`numpy.fft.rfftfreq`

Summary

Number of NumPy functions: 2

Number of functions covered by cunumeric: 0

Random Sampling#

NumPy	cunumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.random.BitGenerator`
`numpy.random.Generator`
`numpy.random.MT19937`
`numpy.random.PCG64`
`numpy.random.PCG64DXSM`
`numpy.random.Philox`
`numpy.random.RandomState`
`numpy.random.SFC64`
`numpy.random.SeedSequence`
`numpy.random.seed`	`cunumeric.random.seed`	✓	✓

Summary

Number of NumPy functions: 10

Number of functions covered by cunumeric: 1