Merge branch 'master' into directives

README.md

@@ -5,7 +5,7 @@
 </div>
 
 ---
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+[![Build Status](https://github.com/KernelTuner/kernel_tuner/actions/workflows/test-python-package.yml/badge.svg)](https://github.com/KernelTuner/kernel_tuner/actions/workflows/test-python-package.yml)
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doc/source/observers.rst

@@ -112,3 +112,11 @@ More information about PMT can be found here: https://git.astron.nl/RD/pmt/
 
 
 
+NCUObserver
+~~~~~~~~~~~
+
+The NCUObserver can be used to automatically extract performance counters during tuning using Nvidia's NsightCompute profiler.
+The NCUObserver relies on an intermediate library, which can be found here: https://github.com/nlesc-recruit/nvmetrics
+
+.. autoclass:: kernel_tuner.observers.ncu.NCUObserver
+

examples/cuda/vector_add_observers_ncu.py

@@ -0,0 +1,57 @@
+#!/usr/bin/env python
+"""This is the minimal example from the README"""
+import json
+
+import numpy
+from kernel_tuner import tune_kernel
+from kernel_tuner.observers.ncu import NCUObserver
+
+def tune():
+
+    kernel_string = """
+    __global__ void vector_add(float *c, float *a, float *b, int n) {
+        int i = blockIdx.x * block_size_x + threadIdx.x;
+        if (i<n) {
+            c[i] = a[i] + b[i];
+        }
+    }
+    """
+
+    size = 80000000
+
+    a = numpy.random.randn(size).astype(numpy.float32)
+    b = numpy.random.randn(size).astype(numpy.float32)
+    c = numpy.zeros_like(b)
+    n = numpy.int32(size)
+
+    args = [c, a, b, n]
+
+    tune_params = dict()
+    tune_params["block_size_x"] = [128+64*i for i in range(15)]
+
+    ncu_metrics = ["dram__bytes.sum",                                       # Counter         byte            # of bytes accessed in DRAM
+                   "dram__bytes_read.sum",                                  # Counter         byte            # of bytes read from DRAM
+                   "dram__bytes_write.sum",                                 # Counter         byte            # of bytes written to DRAM
+                   "smsp__sass_thread_inst_executed_op_fadd_pred_on.sum",   # Counter         inst            # of FADD thread instructions executed where all predicates were true
+                   "smsp__sass_thread_inst_executed_op_ffma_pred_on.sum",   # Counter         inst            # of FFMA thread instructions executed where all predicates were true
+                   "smsp__sass_thread_inst_executed_op_fmul_pred_on.sum",   # Counter         inst            # of FMUL thread instructions executed where all predicates were true
+                  ]
+
+    ncuobserver = NCUObserver(metrics=ncu_metrics)
+
+    def total_fp32_flops(p):
+        return p["smsp__sass_thread_inst_executed_op_fadd_pred_on.sum"] + 2 * p["smsp__sass_thread_inst_executed_op_ffma_pred_on.sum"] + p["smsp__sass_thread_inst_executed_op_fmul_pred_on.sum"]
+
+    metrics = dict()
+    metrics["GFLOP/s"] = lambda p: (total_fp32_flops(p) / 1e9) / (p["time"]/1e3)
+    metrics["Expected GFLOP/s"] = lambda p: (size / 1e9) / (p["time"]/1e3)
+    metrics["GB/s"] = lambda p: (p["dram__bytes.sum"] / 1e9) / (p["time"]/1e3)
+    metrics["Expected GB/s"] = lambda p: (size*4*3 / 1e9) / (p["time"]/1e3)
+
+    results, env = tune_kernel("vector_add", kernel_string, size, args, tune_params, observers=[ncuobserver], metrics=metrics, iterations=7)
+
+    return results
+
+
+if __name__ == "__main__":
+    tune()

kernel_tuner/core.py

@@ -23,7 +23,7 @@
 from kernel_tuner.backends.opencl import OpenCLFunctions
 from kernel_tuner.backends.hip import HipFunctions
 from kernel_tuner.observers.nvml import NVMLObserver
-from kernel_tuner.observers.observer import ContinuousObserver, OutputObserver
+from kernel_tuner.observers.observer import ContinuousObserver, OutputObserver, PrologueObserver
 
 try:
     import torch
@@ -314,11 +314,13 @@ def __init__(
             )
         else:
             raise ValueError("Sorry, support for languages other than CUDA, OpenCL, HIP, C, and Fortran is not implemented yet")
+        self.dev = dev
 
         # look for NVMLObserver in observers, if present, enable special tunable parameters through nvml
         self.use_nvml = False
         self.continuous_observers = []
         self.output_observers = []
+        self.prologue_observers = []
         if observers:
             for obs in observers:
                 if isinstance(obs, NVMLObserver):
@@ -328,49 +330,61 @@ def __init__(
                     self.continuous_observers.append(obs.continuous_observer)
                 if isinstance(obs, OutputObserver):
                     self.output_observers.append(obs)
+                if isinstance(obs, PrologueObserver):
+                    self.prologue_observers.append(obs)
 
+        # Take list of observers from self.dev because Backends tend to add their own observer
+        self.benchmark_observers = [
+            obs for obs in self.dev.observers if not isinstance(obs, (ContinuousObserver, PrologueObserver))
+        ]
 
         self.iterations = iterations
 
         self.lang = lang
-        self.dev = dev
         self.units = dev.units
         self.name = dev.name
         self.max_threads = dev.max_threads
         if not quiet:
             print("Using: " + self.dev.name)
 
+    def benchmark_prologue(self, func, gpu_args, threads, grid, result):
+        """Benchmark prologue one kernel execution per PrologueObserver"""
+
+        for obs in self.prologue_observers:
+            self.dev.synchronize()
+            obs.before_start()
+            self.dev.run_kernel(func, gpu_args, threads, grid)
+            self.dev.synchronize()
+            obs.after_finish()
+            result.update(obs.get_results())
+
     def benchmark_default(self, func, gpu_args, threads, grid, result):
-        """Benchmark one kernel execution at a time."""
-        observers = [
-            obs for obs in self.dev.observers if not isinstance(obs, ContinuousObserver)
-        ]
+        """Benchmark one kernel execution for 'iterations' at a time"""
 
         self.dev.synchronize()
         for _ in range(self.iterations):
-            for obs in observers:
+            for obs in self.benchmark_observers:
                 obs.before_start()
             self.dev.synchronize()
             self.dev.start_event()
             self.dev.run_kernel(func, gpu_args, threads, grid)
             self.dev.stop_event()
-            for obs in observers:
+            for obs in self.benchmark_observers:
                 obs.after_start()
             while not self.dev.kernel_finished():
-                for obs in observers:
+                for obs in self.benchmark_observers:
                     obs.during()
                 time.sleep(1e-6)  # one microsecond
             self.dev.synchronize()
-            for obs in observers:
+            for obs in self.benchmark_observers:
                 obs.after_finish()
 
-        for obs in observers:
+        for obs in self.benchmark_observers:
             result.update(obs.get_results())
 
     def benchmark_continuous(self, func, gpu_args, threads, grid, result, duration):
         """Benchmark continuously for at least 'duration' seconds"""
         iterations = int(np.ceil(duration / (result["time"] / 1000)))
-        # print(f"{iterations=} {(result['time']/1000)=}")
         self.dev.synchronize()
         for obs in self.continuous_observers:
             obs.before_start()
@@ -420,9 +434,8 @@ def benchmark(self, func, gpu_args, instance, verbose, objective, skip_nvml_sett
 
         result = {}
         try:
-            self.benchmark_default(
-                func, gpu_args, instance.threads, instance.grid, result
-            )
+            self.benchmark_prologue(func, gpu_args, instance.threads, instance.grid, result)
+            self.benchmark_default(func, gpu_args, instance.threads, instance.grid, result)
 
             if self.continuous_observers:
                 duration = 1

kernel_tuner/observers/__init__.py

@@ -1 +1 @@
-from .observer import BenchmarkObserver, IterationObserver, ContinuousObserver, OutputObserver
+from .observer import BenchmarkObserver, IterationObserver, ContinuousObserver, OutputObserver, PrologueObserver

kernel_tuner/observers/ncu.py

@@ -0,0 +1,41 @@
+from kernel_tuner.observers import PrologueObserver
+
+try:
+    import nvmetrics
+except (ImportError):
+    nvmetrics = None
+
+class NCUObserver(PrologueObserver):
+    """``NCUObserver`` measures performance counters.
+
+        The exact performance counters supported differ per GPU, some examples:
+
+         * "dram__bytes.sum",                                     # Counter         byte            # of bytes accessed in DRAM
+         * "dram__bytes_read.sum",                                # Counter         byte            # of bytes read from DRAM
+         * "dram__bytes_write.sum",                               # Counter         byte            # of bytes written to DRAM
+         * "smsp__sass_thread_inst_executed_op_fadd_pred_on.sum", # Counter         inst            # of FADD thread instructions executed where all predicates were true
+         * "smsp__sass_thread_inst_executed_op_ffma_pred_on.sum", # Counter         inst            # of FFMA thread instructions executed where all predicates were true
+         * "smsp__sass_thread_inst_executed_op_fmul_pred_on.sum", # Counter         inst            # of FMUL thread instructions executed where all predicates were true
+
+        :param metrics: The metrics to observe. This should be a list of strings.
+                        You can use ``ncu --query-metrics`` to get a list of valid metrics.
+        :type metrics: list[str]
+
+    """
+
+    def __init__(self, metrics=None, device=0):
+        if not nvmetrics:
+            raise ImportError("could not import nvmetrics")
+
+        self.metrics = metrics
+        self.device = device
+        self.results = dict()
+
+    def before_start(self):
+        nvmetrics.measureMetricsStart(self.metrics, self.device)
+
+    def after_finish(self):
+        self.results = nvmetrics.measureMetricsStop()
+
+    def get_results(self):
+        return dict(zip(self.metrics, self.results))

kernel_tuner/observers/observer.py

@@ -57,4 +57,15 @@ def process_output(self, answer, output):
         """
         pass
 
+class PrologueObserver(BenchmarkObserver):
+    """Observer that measures something in a seperate kernel invocation prior to the normal benchmark."""
 
+    @abstractmethod
+    def before_start(self):
+        """prologue start is called before the kernel starts"""
+        pass
+
+    @abstractmethod
+    def after_finish(self):
+        """prologue finish is called after the kernel has finished execution"""
+        pass