Draft of Python API for NVBench

The prototype is based on pybind11 to minimize boiler-plate
code needed to deal with move-only semantics of many nvbench
classes.

python/test/run_1.py

@@ -0,0 +1,108 @@
+import sys
+
+import cuda.nvbench as nvbench
+import numpy as np
+from numba import cuda
+
+
+@cuda.jit()
+def kernel(a, b, c):
+    tid = cuda.grid(1)
+    size = len(a)
+
+    if tid < size:
+        c[tid] = a[tid] + b[tid]
+
+
+def getNumbaStream(launch):
+    return cuda.external_stream(launch.getStream().addressof())
+
+
+def add_two(state):
+    # state.skip("Skipping this benchmark for no reason")
+    N = state.getInt64("elements")
+    a = cuda.to_device(np.random.random(N))
+    c = cuda.device_array_like(a)
+
+    state.addGlobalMemoryReads(a.nbytes)
+    state.addGlobalMemoryWrites(c.nbytes)
+
+    nthreads = 256
+    nblocks = (len(a) + nthreads - 1) // nthreads
+
+    # First call locks, can't use async benchmarks until sync tag is supported
+    kernel[nblocks, nthreads](a, a, c)
+    cuda.synchronize()
+
+    def kernel_launcher(launch):
+        stream = getNumbaStream(launch)
+        kernel[nblocks, nthreads, stream](a, a, c)
+
+    state.exec(kernel_launcher, batched=True, sync=True)
+
+
+def add_float(state):
+    N = state.getInt64("elements")
+    v = state.getFloat64("v")
+    name = state.getString("name")
+    a = cuda.to_device(np.random.random(N).astype(np.float32))
+    b = cuda.to_device(np.random.random(N).astype(np.float32))
+    c = cuda.device_array_like(a)
+
+    state.addGlobalMemoryReads(a.nbytes + b.nbytes)
+    state.addGlobalMemoryWrites(c.nbytes)
+
+    nthreads = 64
+    nblocks = (len(a) + nthreads - 1) // nthreads
+
+    def kernel_launcher(launch):
+        _ = v
+        _ = name
+        stream = getNumbaStream(launch)
+        kernel[nblocks, nthreads, stream](a, b, c)
+
+    state.exec(kernel_launcher, batched=True, sync=True)
+
+
+def add_three(state):
+    N = state.getInt64("elements")
+    a = cuda.to_device(np.random.random(N).astype(np.float32))
+    b = cuda.to_device(np.random.random(N).astype(np.float32))
+    c = cuda.device_array_like(a)
+
+    state.addGlobalMemoryReads(a.nbytes + b.nbytes)
+    state.addGlobalMemoryWrites(c.nbytes)
+
+    nthreads = 256
+    nblocks = (len(a) + nthreads - 1) // nthreads
+
+    def kernel_launcher(launch):
+        stream = getNumbaStream(launch)
+        kernel[nblocks, nthreads, stream](a, b, c)
+
+    state.exec(kernel_launcher, batched=True, sync=True)
+    cuda.synchronize()
+
+
+def register_benchmarks():
+    (
+        nvbench.register(add_two).addInt64Axis(
+            "elements", [2**pow2 for pow2 in range(20, 23)]
+        )
+    )
+    (
+        nvbench.register(add_float)
+        .addFloat64Axis("v", [0.1, 0.3])
+        .addStringAxis("name", ["Anne", "Lynda"])
+        .addInt64Axis("elements", [2**pow2 for pow2 in range(20, 23)])
+    )
+    (
+        nvbench.register(add_three).addInt64Axis(
+            "elements", [2**pow2 for pow2 in range(20, 22)]
+        )
+    )
+
+
+if __name__ == "__main__":
+    register_benchmarks()
+    nvbench.run_all_benchmarks(sys.argv)