# Cuda.jit and njit giving different results

**URL:** <https://numba.discourse.group/t/cuda-jit-and-njit-giving-different-results/2751>\
**Category:** Support: How do I do ...?\
**Created:** [October 1, 2024, 7:21pm UTC](https://numba.discourse.group/t/cuda-jit-and-njit-giving-different-results/2751 "2024-10-01T19:21:38Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**Author:** ![seanlaw](https://yyz2.discourse-cdn.com/free1/user_avatar/numba.discourse.group/seanlaw/32/506_2.png) [@seanlaw](https://numba.discourse.group/u/seanlaw)\
**Post date:** [October 1, 2024, 7:26pm UTC](https://numba.discourse.group/t/cuda-jit-and-njit-giving-different-results/2751/2 "2024-10-01T19:26:15Z")

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For completeness, I attempted to perform the same computation using the `mpmath` package which claims to provide fixed precision capabilities:

```auto
import mpmath
mpmath.mp.dps = 100 # Precision

def mpmath_cpu_func(a, b, c, d):
    for i in range(a.rows):
        for l in range(d[i], 0, -1):
            for j in range(l):
                a[i, j] = (b[i] * a[i, j] + (1.0 - b[i]) * a[i, j + 1]) / c[i]
    return a[:, 0]

# Convert inputs to mpmath types with high precision
mp_a = mpmath.matrix(a.copy())
for i in range(a.shape[0]):
    for j in range(a.shape[1]):
        mp_a[i, j] = mpmath.mpf(a[i, j].astype(str))
mp_b = [mpmath.mpf(b[0].astype(str))]
mp_c = [mpmath.mpf(c[0].astype(str))]

mpmath_cpu_func(mp_a, mp_b, mp_c, d) # Produces 0.6449015342958763156413719663014237700252472529553791866336058829452386808983049961922292904843391669

```

Note that the high precision result is also different from the CPU and GPU results above.

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