793 lines
26 KiB
C++
793 lines
26 KiB
C++
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// Copyright 2017 Google Inc. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// https://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "absl/random/internal/nanobenchmark.h"
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#include <sys/types.h>
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#include <algorithm> // sort
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#include <atomic>
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#include <cstddef>
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#include <cstdint>
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#include <cstdlib>
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#include <cstring> // memcpy
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#include <limits>
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#include <string>
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#include <utility>
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#include <vector>
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#include "absl/base/internal/raw_logging.h"
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#include "absl/random/internal/platform.h"
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#include "absl/random/internal/randen_engine.h"
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// OS
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#if defined(_WIN32) || defined(_WIN64)
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#define ABSL_OS_WIN
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#include <windows.h> // NOLINT
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#elif defined(__ANDROID__)
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#define ABSL_OS_ANDROID
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#elif defined(__linux__)
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#define ABSL_OS_LINUX
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#include <sched.h> // NOLINT
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#include <sys/syscall.h> // NOLINT
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#endif
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#if defined(ABSL_ARCH_X86_64) && !defined(ABSL_OS_WIN)
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#include <cpuid.h> // NOLINT
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#endif
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// __ppc_get_timebase_freq
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#if defined(ABSL_ARCH_PPC)
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#include <sys/platform/ppc.h> // NOLINT
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#endif
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// clock_gettime
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#if defined(ABSL_ARCH_ARM) || defined(ABSL_ARCH_AARCH64)
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#include <time.h> // NOLINT
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#endif
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namespace absl {
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namespace random_internal_nanobenchmark {
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namespace {
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// For code folding.
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namespace platform {
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#if defined(ABSL_ARCH_X86_64)
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// TODO(janwas): Merge with the one in randen_hwaes.cc?
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void Cpuid(const uint32_t level, const uint32_t count,
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uint32_t* ABSL_RANDOM_INTERNAL_RESTRICT abcd) {
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#if defined(ABSL_OS_WIN)
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int regs[4];
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__cpuidex(regs, level, count);
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for (int i = 0; i < 4; ++i) {
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abcd[i] = regs[i];
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}
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#else
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uint32_t a, b, c, d;
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__cpuid_count(level, count, a, b, c, d);
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abcd[0] = a;
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abcd[1] = b;
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abcd[2] = c;
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abcd[3] = d;
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#endif
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}
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std::string BrandString() {
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char brand_string[49];
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uint32_t abcd[4];
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// Check if brand std::string is supported (it is on all reasonable Intel/AMD)
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Cpuid(0x80000000U, 0, abcd);
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if (abcd[0] < 0x80000004U) {
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return std::string();
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}
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for (int i = 0; i < 3; ++i) {
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Cpuid(0x80000002U + i, 0, abcd);
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memcpy(brand_string + i * 16, &abcd, sizeof(abcd));
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}
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brand_string[48] = 0;
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return brand_string;
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}
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// Returns the frequency quoted inside the brand string. This does not
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// account for throttling nor Turbo Boost.
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double NominalClockRate() {
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const std::string& brand_string = BrandString();
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// Brand strings include the maximum configured frequency. These prefixes are
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// defined by Intel CPUID documentation.
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const char* prefixes[3] = {"MHz", "GHz", "THz"};
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const double multipliers[3] = {1E6, 1E9, 1E12};
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for (size_t i = 0; i < 3; ++i) {
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const size_t pos_prefix = brand_string.find(prefixes[i]);
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if (pos_prefix != std::string::npos) {
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const size_t pos_space = brand_string.rfind(' ', pos_prefix - 1);
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if (pos_space != std::string::npos) {
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const std::string digits =
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brand_string.substr(pos_space + 1, pos_prefix - pos_space - 1);
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return std::stod(digits) * multipliers[i];
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}
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}
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}
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return 0.0;
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}
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#endif // ABSL_ARCH_X86_64
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} // namespace platform
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// Prevents the compiler from eliding the computations that led to "output".
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template <class T>
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inline void PreventElision(T&& output) {
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#ifndef ABSL_OS_WIN
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// Works by indicating to the compiler that "output" is being read and
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// modified. The +r constraint avoids unnecessary writes to memory, but only
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// works for built-in types (typically FuncOutput).
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asm volatile("" : "+r"(output) : : "memory");
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#else
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// MSVC does not support inline assembly anymore (and never supported GCC's
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// RTL constraints). Self-assignment with #pragma optimize("off") might be
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// expected to prevent elision, but it does not with MSVC 2015. Type-punning
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// with volatile pointers generates inefficient code on MSVC 2017.
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static std::atomic<T> dummy(T{});
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dummy.store(output, std::memory_order_relaxed);
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#endif
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}
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namespace timer {
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// Start/Stop return absolute timestamps and must be placed immediately before
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// and after the region to measure. We provide separate Start/Stop functions
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// because they use different fences.
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//
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// Background: RDTSC is not 'serializing'; earlier instructions may complete
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// after it, and/or later instructions may complete before it. 'Fences' ensure
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// regions' elapsed times are independent of such reordering. The only
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// documented unprivileged serializing instruction is CPUID, which acts as a
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// full fence (no reordering across it in either direction). Unfortunately
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// the latency of CPUID varies wildly (perhaps made worse by not initializing
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// its EAX input). Because it cannot reliably be deducted from the region's
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// elapsed time, it must not be included in the region to measure (i.e.
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// between the two RDTSC).
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//
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// The newer RDTSCP is sometimes described as serializing, but it actually
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// only serves as a half-fence with release semantics. Although all
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// instructions in the region will complete before the final timestamp is
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// captured, subsequent instructions may leak into the region and increase the
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// elapsed time. Inserting another fence after the final RDTSCP would prevent
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// such reordering without affecting the measured region.
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//
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// Fortunately, such a fence exists. The LFENCE instruction is only documented
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// to delay later loads until earlier loads are visible. However, Intel's
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// reference manual says it acts as a full fence (waiting until all earlier
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// instructions have completed, and delaying later instructions until it
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// completes). AMD assigns the same behavior to MFENCE.
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//
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// We need a fence before the initial RDTSC to prevent earlier instructions
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// from leaking into the region, and arguably another after RDTSC to avoid
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// region instructions from completing before the timestamp is recorded.
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// When surrounded by fences, the additional RDTSCP half-fence provides no
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// benefit, so the initial timestamp can be recorded via RDTSC, which has
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// lower overhead than RDTSCP because it does not read TSC_AUX. In summary,
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// we define Start = LFENCE/RDTSC/LFENCE; Stop = RDTSCP/LFENCE.
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//
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// Using Start+Start leads to higher variance and overhead than Stop+Stop.
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// However, Stop+Stop includes an LFENCE in the region measurements, which
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// adds a delay dependent on earlier loads. The combination of Start+Stop
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// is faster than Start+Start and more consistent than Stop+Stop because
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// the first LFENCE already delayed subsequent loads before the measured
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// region. This combination seems not to have been considered in prior work:
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// http://akaros.cs.berkeley.edu/lxr/akaros/kern/arch/x86/rdtsc_test.c
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//
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// Note: performance counters can measure 'exact' instructions-retired or
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// (unhalted) cycle counts. The RDPMC instruction is not serializing and also
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// requires fences. Unfortunately, it is not accessible on all OSes and we
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// prefer to avoid kernel-mode drivers. Performance counters are also affected
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// by several under/over-count errata, so we use the TSC instead.
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// Returns a 64-bit timestamp in unit of 'ticks'; to convert to seconds,
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// divide by InvariantTicksPerSecond.
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inline uint64_t Start64() {
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uint64_t t;
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#if defined(ABSL_ARCH_PPC)
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asm volatile("mfspr %0, %1" : "=r"(t) : "i"(268));
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#elif defined(ABSL_ARCH_X86_64)
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#if defined(ABSL_OS_WIN)
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_ReadWriteBarrier();
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_mm_lfence();
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_ReadWriteBarrier();
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t = __rdtsc();
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_ReadWriteBarrier();
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_mm_lfence();
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_ReadWriteBarrier();
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#else
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asm volatile(
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"lfence\n\t"
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"rdtsc\n\t"
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"shl $32, %%rdx\n\t"
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"or %%rdx, %0\n\t"
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"lfence"
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: "=a"(t)
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:
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// "memory" avoids reordering. rdx = TSC >> 32.
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// "cc" = flags modified by SHL.
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: "rdx", "memory", "cc");
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#endif
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#else
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// Fall back to OS - unsure how to reliably query cntvct_el0 frequency.
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timespec ts;
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clock_gettime(CLOCK_REALTIME, &ts);
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t = ts.tv_sec * 1000000000LL + ts.tv_nsec;
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#endif
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return t;
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}
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inline uint64_t Stop64() {
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uint64_t t;
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#if defined(ABSL_ARCH_X86_64)
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#if defined(ABSL_OS_WIN)
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_ReadWriteBarrier();
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unsigned aux;
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t = __rdtscp(&aux);
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_ReadWriteBarrier();
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_mm_lfence();
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_ReadWriteBarrier();
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#else
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// Use inline asm because __rdtscp generates code to store TSC_AUX (ecx).
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asm volatile(
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"rdtscp\n\t"
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"shl $32, %%rdx\n\t"
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"or %%rdx, %0\n\t"
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"lfence"
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: "=a"(t)
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:
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// "memory" avoids reordering. rcx = TSC_AUX. rdx = TSC >> 32.
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// "cc" = flags modified by SHL.
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: "rcx", "rdx", "memory", "cc");
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#endif
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#else
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t = Start64();
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#endif
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return t;
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}
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// Returns a 32-bit timestamp with about 4 cycles less overhead than
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// Start64. Only suitable for measuring very short regions because the
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// timestamp overflows about once a second.
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inline uint32_t Start32() {
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uint32_t t;
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#if defined(ABSL_ARCH_X86_64)
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#if defined(ABSL_OS_WIN)
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_ReadWriteBarrier();
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_mm_lfence();
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_ReadWriteBarrier();
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t = static_cast<uint32_t>(__rdtsc());
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_ReadWriteBarrier();
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_mm_lfence();
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_ReadWriteBarrier();
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#else
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asm volatile(
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"lfence\n\t"
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"rdtsc\n\t"
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"lfence"
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: "=a"(t)
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:
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// "memory" avoids reordering. rdx = TSC >> 32.
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: "rdx", "memory");
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#endif
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#else
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t = static_cast<uint32_t>(Start64());
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#endif
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return t;
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}
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inline uint32_t Stop32() {
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uint32_t t;
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#if defined(ABSL_ARCH_X86_64)
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#if defined(ABSL_OS_WIN)
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_ReadWriteBarrier();
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unsigned aux;
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t = static_cast<uint32_t>(__rdtscp(&aux));
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_ReadWriteBarrier();
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_mm_lfence();
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_ReadWriteBarrier();
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#else
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// Use inline asm because __rdtscp generates code to store TSC_AUX (ecx).
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asm volatile(
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"rdtscp\n\t"
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"lfence"
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: "=a"(t)
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:
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// "memory" avoids reordering. rcx = TSC_AUX. rdx = TSC >> 32.
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: "rcx", "rdx", "memory");
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#endif
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#else
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t = static_cast<uint32_t>(Stop64());
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#endif
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return t;
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}
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} // namespace timer
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namespace robust_statistics {
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// Sorts integral values in ascending order (e.g. for Mode). About 3x faster
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// than std::sort for input distributions with very few unique values.
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template <class T>
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void CountingSort(T* values, size_t num_values) {
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// Unique values and their frequency (similar to flat_map).
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using Unique = std::pair<T, int>;
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std::vector<Unique> unique;
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for (size_t i = 0; i < num_values; ++i) {
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const T value = values[i];
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const auto pos =
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std::find_if(unique.begin(), unique.end(),
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[value](const Unique u) { return u.first == value; });
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if (pos == unique.end()) {
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unique.push_back(std::make_pair(value, 1));
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} else {
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++pos->second;
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}
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}
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// Sort in ascending order of value (pair.first).
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std::sort(unique.begin(), unique.end());
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// Write that many copies of each unique value to the array.
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T* ABSL_RANDOM_INTERNAL_RESTRICT p = values;
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for (const auto& value_count : unique) {
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std::fill(p, p + value_count.second, value_count.first);
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p += value_count.second;
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}
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ABSL_RAW_CHECK(p == values + num_values, "Did not produce enough output");
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}
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// @return i in [idx_begin, idx_begin + half_count) that minimizes
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// sorted[i + half_count] - sorted[i].
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template <typename T>
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size_t MinRange(const T* const ABSL_RANDOM_INTERNAL_RESTRICT sorted,
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const size_t idx_begin, const size_t half_count) {
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T min_range = (std::numeric_limits<T>::max)();
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size_t min_idx = 0;
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for (size_t idx = idx_begin; idx < idx_begin + half_count; ++idx) {
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ABSL_RAW_CHECK(sorted[idx] <= sorted[idx + half_count], "Not sorted");
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const T range = sorted[idx + half_count] - sorted[idx];
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if (range < min_range) {
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min_range = range;
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min_idx = idx;
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}
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}
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return min_idx;
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}
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// Returns an estimate of the mode by calling MinRange on successively
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// halved intervals. "sorted" must be in ascending order. This is the
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// Half Sample Mode estimator proposed by Bickel in "On a fast, robust
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// estimator of the mode", with complexity O(N log N). The mode is less
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// affected by outliers in highly-skewed distributions than the median.
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// The averaging operation below assumes "T" is an unsigned integer type.
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template <typename T>
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T ModeOfSorted(const T* const ABSL_RANDOM_INTERNAL_RESTRICT sorted,
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const size_t num_values) {
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size_t idx_begin = 0;
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size_t half_count = num_values / 2;
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while (half_count > 1) {
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idx_begin = MinRange(sorted, idx_begin, half_count);
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half_count >>= 1;
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}
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const T x = sorted[idx_begin + 0];
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if (half_count == 0) {
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return x;
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}
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ABSL_RAW_CHECK(half_count == 1, "Should stop at half_count=1");
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const T average = (x + sorted[idx_begin + 1] + 1) / 2;
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return average;
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}
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// Returns the mode. Side effect: sorts "values".
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template <typename T>
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T Mode(T* values, const size_t num_values) {
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CountingSort(values, num_values);
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return ModeOfSorted(values, num_values);
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|
}
|
||
|
|
||
|
template <typename T, size_t N>
|
||
|
T Mode(T (&values)[N]) {
|
||
|
return Mode(&values[0], N);
|
||
|
}
|
||
|
|
||
|
// Returns the median value. Side effect: sorts "values".
|
||
|
template <typename T>
|
||
|
T Median(T* values, const size_t num_values) {
|
||
|
ABSL_RAW_CHECK(num_values != 0, "Empty input");
|
||
|
std::sort(values, values + num_values);
|
||
|
const size_t half = num_values / 2;
|
||
|
// Odd count: return middle
|
||
|
if (num_values % 2) {
|
||
|
return values[half];
|
||
|
}
|
||
|
// Even count: return average of middle two.
|
||
|
return (values[half] + values[half - 1] + 1) / 2;
|
||
|
}
|
||
|
|
||
|
// Returns a robust measure of variability.
|
||
|
template <typename T>
|
||
|
T MedianAbsoluteDeviation(const T* values, const size_t num_values,
|
||
|
const T median) {
|
||
|
ABSL_RAW_CHECK(num_values != 0, "Empty input");
|
||
|
std::vector<T> abs_deviations;
|
||
|
abs_deviations.reserve(num_values);
|
||
|
for (size_t i = 0; i < num_values; ++i) {
|
||
|
const int64_t abs = std::abs(int64_t(values[i]) - int64_t(median));
|
||
|
abs_deviations.push_back(static_cast<T>(abs));
|
||
|
}
|
||
|
return Median(abs_deviations.data(), num_values);
|
||
|
}
|
||
|
|
||
|
} // namespace robust_statistics
|
||
|
|
||
|
// Ticks := platform-specific timer values (CPU cycles on x86). Must be
|
||
|
// unsigned to guarantee wraparound on overflow. 32 bit timers are faster to
|
||
|
// read than 64 bit.
|
||
|
using Ticks = uint32_t;
|
||
|
|
||
|
// Returns timer overhead / minimum measurable difference.
|
||
|
Ticks TimerResolution() {
|
||
|
// Nested loop avoids exceeding stack/L1 capacity.
|
||
|
Ticks repetitions[Params::kTimerSamples];
|
||
|
for (size_t rep = 0; rep < Params::kTimerSamples; ++rep) {
|
||
|
Ticks samples[Params::kTimerSamples];
|
||
|
for (size_t i = 0; i < Params::kTimerSamples; ++i) {
|
||
|
const Ticks t0 = timer::Start32();
|
||
|
const Ticks t1 = timer::Stop32();
|
||
|
samples[i] = t1 - t0;
|
||
|
}
|
||
|
repetitions[rep] = robust_statistics::Mode(samples);
|
||
|
}
|
||
|
return robust_statistics::Mode(repetitions);
|
||
|
}
|
||
|
|
||
|
static const Ticks timer_resolution = TimerResolution();
|
||
|
|
||
|
// Estimates the expected value of "lambda" values with a variable number of
|
||
|
// samples until the variability "rel_mad" is less than "max_rel_mad".
|
||
|
template <class Lambda>
|
||
|
Ticks SampleUntilStable(const double max_rel_mad, double* rel_mad,
|
||
|
const Params& p, const Lambda& lambda) {
|
||
|
auto measure_duration = [&lambda]() -> Ticks {
|
||
|
const Ticks t0 = timer::Start32();
|
||
|
lambda();
|
||
|
const Ticks t1 = timer::Stop32();
|
||
|
return t1 - t0;
|
||
|
};
|
||
|
|
||
|
// Choose initial samples_per_eval based on a single estimated duration.
|
||
|
Ticks est = measure_duration();
|
||
|
static const double ticks_per_second = InvariantTicksPerSecond();
|
||
|
const size_t ticks_per_eval = ticks_per_second * p.seconds_per_eval;
|
||
|
size_t samples_per_eval = ticks_per_eval / est;
|
||
|
samples_per_eval = (std::max)(samples_per_eval, p.min_samples_per_eval);
|
||
|
|
||
|
std::vector<Ticks> samples;
|
||
|
samples.reserve(1 + samples_per_eval);
|
||
|
samples.push_back(est);
|
||
|
|
||
|
// Percentage is too strict for tiny differences, so also allow a small
|
||
|
// absolute "median absolute deviation".
|
||
|
const Ticks max_abs_mad = (timer_resolution + 99) / 100;
|
||
|
*rel_mad = 0.0; // ensure initialized
|
||
|
|
||
|
for (size_t eval = 0; eval < p.max_evals; ++eval, samples_per_eval *= 2) {
|
||
|
samples.reserve(samples.size() + samples_per_eval);
|
||
|
for (size_t i = 0; i < samples_per_eval; ++i) {
|
||
|
const Ticks r = measure_duration();
|
||
|
samples.push_back(r);
|
||
|
}
|
||
|
|
||
|
if (samples.size() >= p.min_mode_samples) {
|
||
|
est = robust_statistics::Mode(samples.data(), samples.size());
|
||
|
} else {
|
||
|
// For "few" (depends also on the variance) samples, Median is safer.
|
||
|
est = robust_statistics::Median(samples.data(), samples.size());
|
||
|
}
|
||
|
ABSL_RAW_CHECK(est != 0, "Estimator returned zero duration");
|
||
|
|
||
|
// Median absolute deviation (mad) is a robust measure of 'variability'.
|
||
|
const Ticks abs_mad = robust_statistics::MedianAbsoluteDeviation(
|
||
|
samples.data(), samples.size(), est);
|
||
|
*rel_mad = static_cast<double>(static_cast<int>(abs_mad)) / est;
|
||
|
|
||
|
if (*rel_mad <= max_rel_mad || abs_mad <= max_abs_mad) {
|
||
|
if (p.verbose) {
|
||
|
ABSL_RAW_LOG(INFO,
|
||
|
"%6zu samples => %5u (abs_mad=%4u, rel_mad=%4.2f%%)\n",
|
||
|
samples.size(), est, abs_mad, *rel_mad * 100.0);
|
||
|
}
|
||
|
return est;
|
||
|
}
|
||
|
}
|
||
|
|
||
|
if (p.verbose) {
|
||
|
ABSL_RAW_LOG(WARNING,
|
||
|
"rel_mad=%4.2f%% still exceeds %4.2f%% after %6zu samples.\n",
|
||
|
*rel_mad * 100.0, max_rel_mad * 100.0, samples.size());
|
||
|
}
|
||
|
return est;
|
||
|
}
|
||
|
|
||
|
using InputVec = std::vector<FuncInput>;
|
||
|
|
||
|
// Returns vector of unique input values.
|
||
|
InputVec UniqueInputs(const FuncInput* inputs, const size_t num_inputs) {
|
||
|
InputVec unique(inputs, inputs + num_inputs);
|
||
|
std::sort(unique.begin(), unique.end());
|
||
|
unique.erase(std::unique(unique.begin(), unique.end()), unique.end());
|
||
|
return unique;
|
||
|
}
|
||
|
|
||
|
// Returns how often we need to call func for sufficient precision, or zero
|
||
|
// on failure (e.g. the elapsed time is too long for a 32-bit tick count).
|
||
|
size_t NumSkip(const Func func, const void* arg, const InputVec& unique,
|
||
|
const Params& p) {
|
||
|
// Min elapsed ticks for any input.
|
||
|
Ticks min_duration = ~0u;
|
||
|
|
||
|
for (const FuncInput input : unique) {
|
||
|
// Make sure a 32-bit timer is sufficient.
|
||
|
const uint64_t t0 = timer::Start64();
|
||
|
PreventElision(func(arg, input));
|
||
|
const uint64_t t1 = timer::Stop64();
|
||
|
const uint64_t elapsed = t1 - t0;
|
||
|
if (elapsed >= (1ULL << 30)) {
|
||
|
ABSL_RAW_LOG(WARNING,
|
||
|
"Measurement failed: need 64-bit timer for input=%zu\n",
|
||
|
static_cast<size_t>(input));
|
||
|
return 0;
|
||
|
}
|
||
|
|
||
|
double rel_mad;
|
||
|
const Ticks total = SampleUntilStable(
|
||
|
p.target_rel_mad, &rel_mad, p,
|
||
|
[func, arg, input]() { PreventElision(func(arg, input)); });
|
||
|
min_duration = (std::min)(min_duration, total - timer_resolution);
|
||
|
}
|
||
|
|
||
|
// Number of repetitions required to reach the target resolution.
|
||
|
const size_t max_skip = p.precision_divisor;
|
||
|
// Number of repetitions given the estimated duration.
|
||
|
const size_t num_skip =
|
||
|
min_duration == 0 ? 0 : (max_skip + min_duration - 1) / min_duration;
|
||
|
if (p.verbose) {
|
||
|
ABSL_RAW_LOG(INFO, "res=%u max_skip=%zu min_dur=%u num_skip=%zu\n",
|
||
|
timer_resolution, max_skip, min_duration, num_skip);
|
||
|
}
|
||
|
return num_skip;
|
||
|
}
|
||
|
|
||
|
// Replicates inputs until we can omit "num_skip" occurrences of an input.
|
||
|
InputVec ReplicateInputs(const FuncInput* inputs, const size_t num_inputs,
|
||
|
const size_t num_unique, const size_t num_skip,
|
||
|
const Params& p) {
|
||
|
InputVec full;
|
||
|
if (num_unique == 1) {
|
||
|
full.assign(p.subset_ratio * num_skip, inputs[0]);
|
||
|
return full;
|
||
|
}
|
||
|
|
||
|
full.reserve(p.subset_ratio * num_skip * num_inputs);
|
||
|
for (size_t i = 0; i < p.subset_ratio * num_skip; ++i) {
|
||
|
full.insert(full.end(), inputs, inputs + num_inputs);
|
||
|
}
|
||
|
absl::random_internal::randen_engine<uint32_t> rng;
|
||
|
std::shuffle(full.begin(), full.end(), rng);
|
||
|
return full;
|
||
|
}
|
||
|
|
||
|
// Copies the "full" to "subset" in the same order, but with "num_skip"
|
||
|
// randomly selected occurrences of "input_to_skip" removed.
|
||
|
void FillSubset(const InputVec& full, const FuncInput input_to_skip,
|
||
|
const size_t num_skip, InputVec* subset) {
|
||
|
const size_t count = std::count(full.begin(), full.end(), input_to_skip);
|
||
|
// Generate num_skip random indices: which occurrence to skip.
|
||
|
std::vector<uint32_t> omit;
|
||
|
// Replacement for std::iota, not yet available in MSVC builds.
|
||
|
omit.reserve(count);
|
||
|
for (size_t i = 0; i < count; ++i) {
|
||
|
omit.push_back(i);
|
||
|
}
|
||
|
// omit[] is the same on every call, but that's OK because they identify the
|
||
|
// Nth instance of input_to_skip, so the position within full[] differs.
|
||
|
absl::random_internal::randen_engine<uint32_t> rng;
|
||
|
std::shuffle(omit.begin(), omit.end(), rng);
|
||
|
omit.resize(num_skip);
|
||
|
std::sort(omit.begin(), omit.end());
|
||
|
|
||
|
uint32_t occurrence = ~0u; // 0 after preincrement
|
||
|
size_t idx_omit = 0; // cursor within omit[]
|
||
|
size_t idx_subset = 0; // cursor within *subset
|
||
|
for (const FuncInput next : full) {
|
||
|
if (next == input_to_skip) {
|
||
|
++occurrence;
|
||
|
// Haven't removed enough already
|
||
|
if (idx_omit < num_skip) {
|
||
|
// This one is up for removal
|
||
|
if (occurrence == omit[idx_omit]) {
|
||
|
++idx_omit;
|
||
|
continue;
|
||
|
}
|
||
|
}
|
||
|
}
|
||
|
if (idx_subset < subset->size()) {
|
||
|
(*subset)[idx_subset++] = next;
|
||
|
}
|
||
|
}
|
||
|
ABSL_RAW_CHECK(idx_subset == subset->size(), "idx_subset not at end");
|
||
|
ABSL_RAW_CHECK(idx_omit == omit.size(), "idx_omit not at end");
|
||
|
ABSL_RAW_CHECK(occurrence == count - 1, "occurrence not at end");
|
||
|
}
|
||
|
|
||
|
// Returns total ticks elapsed for all inputs.
|
||
|
Ticks TotalDuration(const Func func, const void* arg, const InputVec* inputs,
|
||
|
const Params& p, double* max_rel_mad) {
|
||
|
double rel_mad;
|
||
|
const Ticks duration =
|
||
|
SampleUntilStable(p.target_rel_mad, &rel_mad, p, [func, arg, inputs]() {
|
||
|
for (const FuncInput input : *inputs) {
|
||
|
PreventElision(func(arg, input));
|
||
|
}
|
||
|
});
|
||
|
*max_rel_mad = (std::max)(*max_rel_mad, rel_mad);
|
||
|
return duration;
|
||
|
}
|
||
|
|
||
|
// (Nearly) empty Func for measuring timer overhead/resolution.
|
||
|
ABSL_ATTRIBUTE_NEVER_INLINE FuncOutput EmptyFunc(const void* arg,
|
||
|
const FuncInput input) {
|
||
|
return input;
|
||
|
}
|
||
|
|
||
|
// Returns overhead of accessing inputs[] and calling a function; this will
|
||
|
// be deducted from future TotalDuration return values.
|
||
|
Ticks Overhead(const void* arg, const InputVec* inputs, const Params& p) {
|
||
|
double rel_mad;
|
||
|
// Zero tolerance because repeatability is crucial and EmptyFunc is fast.
|
||
|
return SampleUntilStable(0.0, &rel_mad, p, [arg, inputs]() {
|
||
|
for (const FuncInput input : *inputs) {
|
||
|
PreventElision(EmptyFunc(arg, input));
|
||
|
}
|
||
|
});
|
||
|
}
|
||
|
|
||
|
} // namespace
|
||
|
|
||
|
void PinThreadToCPU(int cpu) {
|
||
|
// We might migrate to another CPU before pinning below, but at least cpu
|
||
|
// will be one of the CPUs on which this thread ran.
|
||
|
#if defined(ABSL_OS_WIN)
|
||
|
if (cpu < 0) {
|
||
|
cpu = static_cast<int>(GetCurrentProcessorNumber());
|
||
|
ABSL_RAW_CHECK(cpu >= 0, "PinThreadToCPU detect failed");
|
||
|
if (cpu >= 64) {
|
||
|
// NOTE: On wine, at least, GetCurrentProcessorNumber() sometimes returns
|
||
|
// a value > 64, which is out of range. When this happens, log a message
|
||
|
// and don't set a cpu affinity.
|
||
|
ABSL_RAW_LOG(ERROR, "Invalid CPU number: %d", cpu);
|
||
|
return;
|
||
|
}
|
||
|
} else if (cpu >= 64) {
|
||
|
// User specified an explicit CPU affinity > the valid range.
|
||
|
ABSL_RAW_LOG(FATAL, "Invalid CPU number: %d", cpu);
|
||
|
}
|
||
|
const DWORD_PTR prev = SetThreadAffinityMask(GetCurrentThread(), 1ULL << cpu);
|
||
|
ABSL_RAW_CHECK(prev != 0, "SetAffinity failed");
|
||
|
#elif defined(ABSL_OS_LINUX) && !defined(ABSL_OS_ANDROID)
|
||
|
if (cpu < 0) {
|
||
|
cpu = sched_getcpu();
|
||
|
ABSL_RAW_CHECK(cpu >= 0, "PinThreadToCPU detect failed");
|
||
|
}
|
||
|
const pid_t pid = 0; // current thread
|
||
|
cpu_set_t set;
|
||
|
CPU_ZERO(&set);
|
||
|
CPU_SET(cpu, &set);
|
||
|
const int err = sched_setaffinity(pid, sizeof(set), &set);
|
||
|
ABSL_RAW_CHECK(err == 0, "SetAffinity failed");
|
||
|
#endif
|
||
|
}
|
||
|
|
||
|
// Returns tick rate. Invariant means the tick counter frequency is independent
|
||
|
// of CPU throttling or sleep. May be expensive, caller should cache the result.
|
||
|
double InvariantTicksPerSecond() {
|
||
|
#if defined(ABSL_ARCH_PPC)
|
||
|
return __ppc_get_timebase_freq();
|
||
|
#elif defined(ABSL_ARCH_X86_64)
|
||
|
// We assume the TSC is invariant; it is on all recent Intel/AMD CPUs.
|
||
|
return platform::NominalClockRate();
|
||
|
#else
|
||
|
// Fall back to clock_gettime nanoseconds.
|
||
|
return 1E9;
|
||
|
#endif
|
||
|
}
|
||
|
|
||
|
size_t MeasureImpl(const Func func, const void* arg, const size_t num_skip,
|
||
|
const InputVec& unique, const InputVec& full,
|
||
|
const Params& p, Result* results) {
|
||
|
const float mul = 1.0f / static_cast<int>(num_skip);
|
||
|
|
||
|
InputVec subset(full.size() - num_skip);
|
||
|
const Ticks overhead = Overhead(arg, &full, p);
|
||
|
const Ticks overhead_skip = Overhead(arg, &subset, p);
|
||
|
if (overhead < overhead_skip) {
|
||
|
ABSL_RAW_LOG(WARNING, "Measurement failed: overhead %u < %u\n", overhead,
|
||
|
overhead_skip);
|
||
|
return 0;
|
||
|
}
|
||
|
|
||
|
if (p.verbose) {
|
||
|
ABSL_RAW_LOG(INFO, "#inputs=%5zu,%5zu overhead=%5u,%5u\n", full.size(),
|
||
|
subset.size(), overhead, overhead_skip);
|
||
|
}
|
||
|
|
||
|
double max_rel_mad = 0.0;
|
||
|
const Ticks total = TotalDuration(func, arg, &full, p, &max_rel_mad);
|
||
|
|
||
|
for (size_t i = 0; i < unique.size(); ++i) {
|
||
|
FillSubset(full, unique[i], num_skip, &subset);
|
||
|
const Ticks total_skip = TotalDuration(func, arg, &subset, p, &max_rel_mad);
|
||
|
|
||
|
if (total < total_skip) {
|
||
|
ABSL_RAW_LOG(WARNING, "Measurement failed: total %u < %u\n", total,
|
||
|
total_skip);
|
||
|
return 0;
|
||
|
}
|
||
|
|
||
|
const Ticks duration = (total - overhead) - (total_skip - overhead_skip);
|
||
|
results[i].input = unique[i];
|
||
|
results[i].ticks = duration * mul;
|
||
|
results[i].variability = max_rel_mad;
|
||
|
}
|
||
|
|
||
|
return unique.size();
|
||
|
}
|
||
|
|
||
|
size_t Measure(const Func func, const void* arg, const FuncInput* inputs,
|
||
|
const size_t num_inputs, Result* results, const Params& p) {
|
||
|
ABSL_RAW_CHECK(num_inputs != 0, "No inputs");
|
||
|
|
||
|
const InputVec unique = UniqueInputs(inputs, num_inputs);
|
||
|
const size_t num_skip = NumSkip(func, arg, unique, p); // never 0
|
||
|
if (num_skip == 0) return 0; // NumSkip already printed error message
|
||
|
|
||
|
const InputVec full =
|
||
|
ReplicateInputs(inputs, num_inputs, unique.size(), num_skip, p);
|
||
|
|
||
|
// MeasureImpl may fail up to p.max_measure_retries times.
|
||
|
for (size_t i = 0; i < p.max_measure_retries; i++) {
|
||
|
auto result = MeasureImpl(func, arg, num_skip, unique, full, p, results);
|
||
|
if (result != 0) {
|
||
|
return result;
|
||
|
}
|
||
|
}
|
||
|
// All retries failed. (Unusual)
|
||
|
return 0;
|
||
|
}
|
||
|
|
||
|
} // namespace random_internal_nanobenchmark
|
||
|
} // namespace absl
|