零和一的字面量 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 6 数学运算和初等函数 33 6.1 算术运算符 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 链式比较 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 初等函数 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 6.6 运算符的优先级与结合性 . . . . . . . . . . . . . . . . . . . . . 40 舍入函数 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 除法函数 . . . . . . . . . . . . . . . . . . . . . . . . .

whether the number of waitersCHAPTER 33. PROFILING 438 Figure 33.1: CPU Profile is excessive given the rate at which work items are being produced in the channel. If we run this, we obtain the following PProf overhead, so a sample_rate argument can be passed to speed it up by making it skip some allocations. Passing sample_rate=1.0 will make it record everything (which is slow); sample_rate=0.1 will record only We collect the profile (specifying a sample rate), then we visualize it. using Profile, PProf Profile.Allocs.clear() Profile.Allocs.@profile sample_rate=0.0001 my_function() PProf.Allocs.pprof() Here

whether the number of waitersCHAPTER 33. PROFILING 439 Figure 33.1: CPU Profile is excessive given the rate at which work items are being produced in the channel. If we run this, we obtain the following PProf overhead, so a sample_rate argument can be passed to speed it up by making it skip some allocations. Passing sample_rate=1.0 will make it record everything (which is slow); sample_rate=0.1 will record only We collect the profile (specifying a sample rate), then we visualize it. using Profile, PProf Profile.Allocs.clear() Profile.Allocs.@profile sample_rate=0.0001 my_function() PProf.Allocs.pprof() Here

whether the number of waitersCHAPTER 33. PROFILING 438 Figure 33.1: CPU Profile is excessive given the rate at which work items are being produced in the channel. If we run this, we obtain the following PProf overhead, so a sample_rate argument can be passed to speed it up by making it skip some allocations. Passing sample_rate=1.0 will make it record everything (which is slow); sample_rate=0.1 will record only We collect the profile (specifying a sample rate), then we visualize it. using Profile, PProf Profile.Allocs.clear() Profile.Allocs.@profile sample_rate=0.0001 my_function() PProf.Allocs.pprof() Here

whether the number of waitersCHAPTER 33. PROFILING 438 Figure 33.1: CPU Profile is excessive given the rate at which work items are being produced in the channel. If we run this, we obtain the following PProf overhead, so a sample_rate argument can be passed to speed it up by making it skip some allocations. Passing sample_rate=1.0 will make it record everything (which is slow); sample_rate=0.1 will record only We collect the profile (specifying a sample rate), then we visualize it. using Profile, PProf Profile.Allocs.clear() Profile.Allocs.@profile sample_rate=0.0001 my_function() PProf.Allocs.pprof() Here

whether the number of waitersCHAPTER 33. PROFILING 438 Figure 33.1: CPU Profile is excessive given the rate at which work items are being produced in the channel. If we run this, we obtain the following PProf overhead, so a sample_rate argument can be passed to speed it up by making it skip some allocations. Passing sample_rate=1.0 will make it record everything (which is slow); sample_rate=0.1 will record only We collect the profile (specifying a sample rate), then we visualize it. using Profile, PProf Profile.Allocs.clear() Profile.Allocs.@profile sample_rate=0.0001 my_function() PProf.Allocs.pprof() Here

overhead, so a sample_rate argument can be passed to speed it up by making it skip some allocations. Passing sample_rate=1.0 will make it record everything (which is slow); sample_rate=0.1 will record only We collect the profile (specifying a sample rate), then we visualize it. using Profile, PProf Profile.Allocs.clear() Profile.Allocs.@profile sample_rate=0.0001 my_function() PProf.Allocs.pprof() Here Here is a more in-depth example, showing how we can tune the sample rate. A good number of samples to aim for is around 1 - 10 thousand. Too many, and the profile visualizer can get overwhelmed, and profiling

overhead, so a sample_rate argument can be passed to speed it up by making it skip some allocations. Passing sample_rate=1.0 will make it record everything (which is slow); sample_rate=0.1 will record only We collect the profile (specifying a sample rate), then we visualize it. using Profile, PProf Profile.Allocs.clear() Profile.Allocs.@profile sample_rate=0.0001 my_function() PProf.Allocs.pprof() Here Here is a more in-depth example, showing how we can tune the sample rate. A good number of samples to aim for is around 1 - 10 thousand. Too many, and the profile visualizer can get overwhelmed, and profiling

overhead, so a sample_rate argument can be passed to speed it up by making it skip some allocations. Passing sample_rate=1.0 will make it record everything (which is slow); sample_rate=0.1 will record only We collect the profile (specifying a sample rate), then we visualize it. using Profile, PProf Profile.Allocs.clear() Profile.Allocs.@profile sample_rate=0.0001 my_function() PProf.Allocs.pprof() Here Here is a more in-depth example, showing how we can tune the sample rate. A good number of samples to aim for is around 1 - 10 thousand. Too many, and the profile visualizer can get overwhelmed, and profiling

overhead, so a sample_rate argument can be passed to speed it up by making it skip some allocations. Passing sample_rate=1.0 will make it record everything (which is slow); sample_rate=0.1 will record only We collect the profile (specifying a sample rate), then we visualize it. using Profile, PProf Profile.Allocs.clear() Profile.Allocs.@profile sample_rate=0.0001 my_function() PProf.Allocs.pprof() Here Here is a more in-depth example, showing how we can tune the sample rate. A good number of samples to aim for is around 1 - 10 thousand. Too many, and the profile visualizer can get overwhelmed, and profiling