aboutsummaryrefslogtreecommitdiffhomepage
path: root/Eigen
Commit message (Collapse)AuthorAge
* Remove vim specific comments to recognoize correct file-type.Gravatar David Tellenbach2021-02-09
| | | | As discussed in #2143 we remove editor specific comments.
* Replace nullptr by NULL in SparseLU.h to be C++03 compliant.Gravatar David Tellenbach2021-02-09
|
* add specialization of check_sparse_solving() for SuperLU solver, in order to ↵Gravatar Ralf Hannemann-Tamas2021-02-08
| | | | test adjoint and transpose solves
* Fix documentation typos in LDLT.hGravatar Nikolaus Demmel2021-02-08
|
* Enable bdcsvd on host.Gravatar Antonio Sanchez2021-02-08
| | | | | | | | | | | | | Currently if compiled by NVCC, the `MatrixBase::bdcSvd()` implementation is skipped, leading to a linker error. This prevents it from running on the host as well. Seems it was disabled 6 years ago (5384e891) to match `jacobiSvd`, but `jacobiSvd` is now enabled on host. Tested and runs fine on host, but will not compile/run for device (though it's not labelled as a device function, so this should be fine). Fixes #2139
* Add more tests for pow and fix a corner case for huge exponent where the ↵Gravatar Rasmus Munk Larsen2021-02-05
| | | | result is always zero or infinite unless x is one.
* Disable vectorized pow for half/bfloat16.Gravatar Antonio Sanchez2021-02-05
| | | | | | | | | We are potentially seeing some accuracy issues with these. Ideally we would hand off to `float`, but that's not trivial with the current setup. We may want to consider adding `ppow<Packet>` and `HasPow`, so implementations can more easily specialize this.
* Fix excessive GEBP register spilling for 32-bit NEON.Gravatar Antonio Sanchez2021-02-03
| | | | | | | | | | | | | | | | | | | | | Clang does a poor job of optimizing the GEBP microkernel on 32-bit ARM, leading to excessive 16-byte register spills, slowing down basic f32 matrix multiplication by approx 50%. By specializing `gebp_traits`, we can eliminate the register spills. Volatile inline ASM both acts as a barrier to prevent reordering and enforces strict register use. In a simple f32 matrix multiply example, this modification reduces 16-byte spills from 109 instances to zero, leading to a 1.5x speed increase (search for `16-byte Spill` in the assembly in https://godbolt.org/z/chsPbE). This is a replacement of !379. See there for further discussion. Also moved `gebp_traits` specializations for NEON to `Eigen/src/Core/arch/NEON/GeneralBlockPanelKernel.h` to be alongside other NEON-specific code. Fixes #2138.
* Eliminate implicit conversions from float to double.Gravatar Antonio Sanchez2021-02-01
|
* Implement bit_* for device.Gravatar Antonio Sanchez2021-02-01
| | | | | | | | | | Unfortunately `std::bit_and` and the like are host-only functions prior to c++14 (since they are not `constexpr`). They also never exist in the global namespace, so the current implementation always fails to compile via NVCC - since `EIGEN_USING_STD` tries to import the symbol from the global namespace on device. To overcome these limitations, we implement these functionals here.
* Fix altivec packetmath.Gravatar Antonio Sanchez2021-01-28
| | | | | | | | | | | | | | | Allows the altivec packetmath tests to pass. There were a few issues: - `pstoreu` was missing MSQ on `_BIG_ENDIAN` systems - `cmp_*` didn't properly handle conversion of bool flags (0x7FC instead of 0xFFFF) - `pfrexp` needed to set the `exponent` argument. Related to !370, #2128 cc: @ChipKerchner @pdrocaldeira Tested on `_BIG_ENDIAN` running on QEMU with VSX. Couldn't figure out build flags to get it to work for little endian.
* Fix clang compilation for AltiVec from previous check-inGravatar Chip Kerchner2021-01-28
|
* Add the following functionsGravatar David Tellenbach2021-01-28
| | | | | | | DenseBase::setConstant(NoChange_t, Index, const Scalar&) DenseBase::setConstant(Index, NoChange_t, const Scalar&) to close #663.
* Add the following functions:Gravatar David Tellenbach2021-01-28
| | | | | | | | | | | DenseBase::setZero(NoChange_t, Index) DenseBase::setZero(Index, NoChange_t) DenseBase::setOnes(NoChange_t, Index) DenseBase::setOnes(Index, NoChange_t) DenseBase::setRandom(NoChange_t, Index) DenseBase::setRandom(Index, NoChange_t) This closes #663.
* Allow for negative strides.Gravatar Gael Guennebaud2021-01-27
| | | | | | | Note that using a stride of -1 is still not possible because it would clash with the definition of Eigen::Dynamic. This fixes #747.
* Include `<cstdint>` in one place, remove custom typedefsGravatar Antonio Sanchez2021-01-26
| | | | | | | | | | | | | | Originating from [this SO issue](https://stackoverflow.com/questions/65901014/how-to-solve-this-all-error-2-in-this-case), some win32 compilers define `__int32` as a `long`, but MinGW defines `std::int32_t` as an `int`, leading to a type conflict. To avoid this, we remove the custom `typedef` definitions for win32. The Tensor module requires C++11 anyways, so we are guaranteed to have included `<cstdint>` already in `Eigen/Core`. Also re-arranged the headers to only include `<cstdint>` in one place to avoid this type of error again.
* Fix sqrt, ldexp and frexp compilation errors.Gravatar Chip Kerchner2021-01-25
|
* Remove unused variable in SparseLU.hGravatar Florian Maurin2021-01-22
|
* Fix pow and other cwise ops for half/bfloat16.Gravatar Antonio Sanchez2021-01-22
| | | | | | | | | | | | | The new `generic_pow` implementation was failing for half/bfloat16 since their construction from int/float is not `constexpr`. Modified in `GenericPacketMathFunctions` to remove `constexpr`. While adding tests for half/bfloat16, found other issues related to implicit conversions. Also needed to implement `numext::arg` for non-integer, non-complex, non-float/double/long double types. These seem to be implicitly converted to `std::complex<T>`, which then fails for half/bfloat16.
* Specialize std::complex operators for use on GPU device.Gravatar Antonio Sanchez2021-01-22
| | | | | | | | | | | | | | | | | | NVCC and older versions of clang do not fully support `std::complex` on device, leading to either compile errors (Cannot call `__host__` function) or worse, runtime errors (Illegal instruction). For most functions, we can implement specialized `numext` versions. Here we specialize the standard operators (with the exception of stream operators and member function operators with a scalar that are already specialized in `<complex>`) so they can be used in device code as well. To import these operators into the current scope, use `EIGEN_USING_STD_COMPLEX_OPERATORS`. By default, these are imported into the `Eigen`, `Eigen:internal`, and `Eigen::numext` namespaces. This allow us to remove specializations of the sum/difference/product/quotient ops, and allow us to treat complex numbers like most other scalars (e.g. in tests).
* Add support for Arm SVEGravatar David Tellenbach2021-01-21
| | | | | | | | | | | | This patch adds support for Arm's new vector extension SVE (Scalable Vector Extension). In contrast to other vector extensions that are supported by Eigen, SVE types are inherently *sizeless*. For the use in Eigen we fix their size at compile-time (note that this is not necessary in general, SVE is *length agnostic*). During compilation the flag `-msve-vector-bits=N` has to be set where `N` is a power of two in the range of `128`to `2048`, indicating the length of an SVE vector. Since SVE is rather young, we decided to disable it by default even if it would be available. A user has to enable it explicitly by defining `EIGEN_ARM64_USE_SVE`. This patch introduces the packet types `PacketXf` and `PacketXi` for packets of `float` and `int32_t` respectively. The size of these packets depends on the SVE vector length. E.g. if `-msve-vector-bits=512` is set, `PacketXf` will contain `512/32 = 16` elements. This MR is joint work with Miguel Tairum <miguel.tairum@arm.com>.
* Fix pfrexp/pldexp for half.Gravatar Antonio Sanchez2021-01-21
| | | | | | | | | | The recent addition of vectorized pow (!330) relies on `pfrexp` and `pldexp`. This was missing for `Eigen::half` and `Eigen::bfloat16`. Adding tests for these packet ops also exposed an issue with handling negative values in `pfrexp`, returning an incorrect exponent. Added the missing implementations, corrected the exponent in `pfrexp1`, and added `packetmath` tests.
* Fix signed-unsigned comparison.Gravatar Antonio Sanchez2021-01-20
| | | | | | | | Hex literals are interpreted as unsigned, leading to a comparison between signed max supported function `abcd[0]` (which was negative) to the unsigned literal `0x80000006`. Should not change result since signed is implicitly converted to unsigned for the comparison, but eliminates the warning.
* Proper CPUIDGravatar Ivan Popivanov2021-01-18
|
* Vectorize `pow(x, y)`. This closes ↵Gravatar Rasmus Munk Larsen2021-01-18
| | | | | | | | | | | | | | | | | | | | | | https://gitlab.com/libeigen/eigen/-/issues/2085, which also contains a description of the algorithm. I ran some testing (comparing to `std::pow(double(x), double(y)))` for `x` in the set of all (positive) floats in the interval `[std::sqrt(std::numeric_limits<float>::min()), std::sqrt(std::numeric_limits<float>::max())]`, and `y` in `{2, sqrt(2), -sqrt(2)}` I get the following error statistics: ``` max_rel_error = 8.34405e-07 rms_rel_error = 2.76654e-07 ``` If I widen the range to all normal float I see lower accuracy for arguments where the result is subnormal, e.g. for `y = sqrt(2)`: ``` max_rel_error = 0.666667 rms = 6.8727e-05 count = 1335165689 argmax = 2.56049e-32, 2.10195e-45 != 1.4013e-45 ``` which seems reasonable, since these results are subnormals with only couple of significant bits left.
* Improved std::complex sqrt and rsqrt.Gravatar Antonio Sanchez2021-01-17
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Replaces `std::sqrt` with `complex_sqrt` for all platforms (previously `complex_sqrt` was only used for CUDA and MSVC), and implements custom `complex_rsqrt`. Also introduces `numext::rsqrt` to simplify implementation, and modified `numext::hypot` to adhere to IEEE IEC 6059 for special cases. The `complex_sqrt` and `complex_rsqrt` implementations were found to be significantly faster than `std::sqrt<std::complex<T>>` and `1/numext::sqrt<std::complex<T>>`. Benchmark file attached. ``` GCC 10, Intel Xeon, x86_64: --------------------------------------------------------------------------- Benchmark Time CPU Iterations --------------------------------------------------------------------------- BM_Sqrt<std::complex<float>> 9.21 ns 9.21 ns 73225448 BM_StdSqrt<std::complex<float>> 17.1 ns 17.1 ns 40966545 BM_Sqrt<std::complex<double>> 8.53 ns 8.53 ns 81111062 BM_StdSqrt<std::complex<double>> 21.5 ns 21.5 ns 32757248 BM_Rsqrt<std::complex<float>> 10.3 ns 10.3 ns 68047474 BM_DivSqrt<std::complex<float>> 16.3 ns 16.3 ns 42770127 BM_Rsqrt<std::complex<double>> 11.3 ns 11.3 ns 61322028 BM_DivSqrt<std::complex<double>> 16.5 ns 16.5 ns 42200711 Clang 11, Intel Xeon, x86_64: --------------------------------------------------------------------------- Benchmark Time CPU Iterations --------------------------------------------------------------------------- BM_Sqrt<std::complex<float>> 7.46 ns 7.45 ns 90742042 BM_StdSqrt<std::complex<float>> 16.6 ns 16.6 ns 42369878 BM_Sqrt<std::complex<double>> 8.49 ns 8.49 ns 81629030 BM_StdSqrt<std::complex<double>> 21.8 ns 21.7 ns 31809588 BM_Rsqrt<std::complex<float>> 8.39 ns 8.39 ns 82933666 BM_DivSqrt<std::complex<float>> 14.4 ns 14.4 ns 48638676 BM_Rsqrt<std::complex<double>> 9.83 ns 9.82 ns 70068956 BM_DivSqrt<std::complex<double>> 15.7 ns 15.7 ns 44487798 Clang 9, Pixel 2, aarch64: --------------------------------------------------------------------------- Benchmark Time CPU Iterations --------------------------------------------------------------------------- BM_Sqrt<std::complex<float>> 24.2 ns 24.1 ns 28616031 BM_StdSqrt<std::complex<float>> 104 ns 103 ns 6826926 BM_Sqrt<std::complex<double>> 31.8 ns 31.8 ns 22157591 BM_StdSqrt<std::complex<double>> 128 ns 128 ns 5437375 BM_Rsqrt<std::complex<float>> 31.9 ns 31.8 ns 22384383 BM_DivSqrt<std::complex<float>> 99.2 ns 98.9 ns 7250438 BM_Rsqrt<std::complex<double>> 46.0 ns 45.8 ns 15338689 BM_DivSqrt<std::complex<double>> 119 ns 119 ns 5898944 ```
* 1)provide a better generic paddsub op implementationGravatar Guoqiang QI2021-01-13
| | | | | 2)make paddsub op support the Packet2cf/Packet4f/Packet2f in NEON 3)make paddsub op support the Packet2cf/Packet4f in SSE
* Remove `inf` local variable.Gravatar Antonio Sanchez2021-01-12
| | | | | | Apparently `inf` is a macro on iOS for `std::numeric_limits<T>::infinity()`, causing a compile error here. We don't need the local anyways since it's only used in one spot.
* Remove TODO from Transform::computeScaleRotation()Gravatar Antonio Sanchez2021-01-11
| | | | | | | Upon investigation, `JacobiSVD` is significantly faster than `BDCSVD` for small matrices (twice as fast for 2x2, 20% faster for 3x3, 1% faster for 10x10). Since the majority of cases will be small, let's stick with `JacobiSVD`. See !361.
* Transform::computeScalingRotation flush determinant to +/- 1.Gravatar Antonio Sanchez2021-01-11
| | | | | | | | In the previous code, in attempting to correct for a negative determinant, we end up multiplying and dividing by a number that is often very near, but not exactly +/-1. By flushing to +/-1, we can replace a division with a multiplication, and results are more numerically consistent.
* Only specialize complex `sqrt_impl` for CUDA if not MSVC.Gravatar Antonio Sanchez2021-01-11
| | | | | We already specialize `sqrt_impl` on windows due to MSVC's mishandling of `inf` (!355).
* Fix for breakage in ROCm support - 210108Gravatar Deven Desai2021-01-08
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | The following commit breaks ROCm support for Eigen https://gitlab.com/libeigen/eigen/-/commit/f149e0ebc3d3d5ca63234e58ca72690caf07e3b5 All unit tests fail with the following error ``` Building HIPCC object test/CMakeFiles/gpu_basic.dir/gpu_basic_generated_gpu_basic.cu.o In file included from /home/rocm-user/eigen/test/gpu_basic.cu:19: In file included from /home/rocm-user/eigen/test/main.h:356: In file included from /home/rocm-user/eigen/Eigen/QR:11: In file included from /home/rocm-user/eigen/Eigen/Core:166: /home/rocm-user/eigen/Eigen/src/Core/MathFunctionsImpl.h:105:35: error: __host__ __device__ function 'complex_sqrt' cannot overload __host__ function 'complex_sqrt' EIGEN_DEVICE_FUNC std::complex<T> complex_sqrt(const std::complex<T>& z) { ^ /home/rocm-user/eigen/Eigen/src/Core/MathFunctions.h:342:38: note: previous declaration is here template<typename T> std::complex<T> complex_sqrt(const std::complex<T>& a_x); ^ 1 error generated when compiling for gfx900. CMake Error at gpu_basic_generated_gpu_basic.cu.o.cmake:192 (message): Error generating file /home/rocm-user/eigen/build/test/CMakeFiles/gpu_basic.dir//./gpu_basic_generated_gpu_basic.cu.o test/CMakeFiles/gpu_basic.dir/build.make:63: recipe for target 'test/CMakeFiles/gpu_basic.dir/gpu_basic_generated_gpu_basic.cu.o' failed make[3]: *** [test/CMakeFiles/gpu_basic.dir/gpu_basic_generated_gpu_basic.cu.o] Error 1 CMakeFiles/Makefile2:16618: recipe for target 'test/CMakeFiles/gpu_basic.dir/all' failed make[2]: *** [test/CMakeFiles/gpu_basic.dir/all] Error 2 CMakeFiles/Makefile2:16625: recipe for target 'test/CMakeFiles/gpu_basic.dir/rule' failed make[1]: *** [test/CMakeFiles/gpu_basic.dir/rule] Error 2 Makefile:5401: recipe for target 'gpu_basic' failed make: *** [gpu_basic] Error 2 ``` The error message is accurate, and the fix (provided in thsi commit) is trivial.
* Fix MSVC complex sqrt and packetmath test.Gravatar Antonio Sanchez2021-01-08
| | | | | | | | | MSVC incorrectly handles `inf` cases for `std::sqrt<std::complex<T>>`. Here we replace it with a custom version (currently used on GPU). Also fixed the `packetmath` test, which previously skipped several corner cases since `CHECK_CWISE1` only tests the first `PacketSize` elements.
* Make Transform::computeRotationScaling(0,&S) continuousGravatar Essex Edwards2021-01-07
|
* Add missing #endif directive in Macros.hGravatar David Tellenbach2021-01-07
|
* #define was defined incorrectly because the result_of function was ↵Gravatar shrek14022021-01-07
| | | | deprecated in c++17 and removed in c++20. Also, EIGEN_COMP_MSVC (which is _MSC_VER) only affects result_of indirectly, which can cause errors.
* Fix Ref initialization.Gravatar Antonio Sanchez2021-01-06
| | | | | | | | | Since `eigen_assert` is a macro, the statements can become noops (e.g. when compiling for GPU), so they may not execute the contained logic -- which in this case is the entire `Ref` construction. We need to separate the assert from statements which have consequences. Fixes #2113
* Allow CwiseUnaryView to be used on device.Gravatar Antonio Sanchez2021-01-06
| | | | Added `EIGEN_DEVICE_FUNC` to methods.
* Fix Ref Stride checks.Gravatar Antonio Sanchez2021-01-05
| | | | | | | | | | | | | | | | | | | | The existing `Ref` class failed to consider cases where the Ref's `Stride` setting *could* match the underlying referred object's stride, but **didn't** at runtime. This led to trying to set invalid stride values, causing runtime failures in some cases, and garbage due to mismatched strides in others. Here we add the missing runtime checks. This involves computing the strides necessary to align with the referred object's storage, and verifying we can actually set those strides at runtime. In the `const` case, if it *may* be possible to refer to the original storage at compile-time but fails at runtime, then we defer to the `construct(...)` method that makes a copy. Added more tests to check these cases. Fixes #2093.
* Eliminate boolean product warnings by factoring out aGravatar Christoph Hertzberg2021-01-05
| | | `combine_scalar_factors` helper function.
* Add CUDA complex sqrt.Gravatar Antonio Sanchez2020-12-22
| | | | | | | | | | | | | | | This is to support scalar `sqrt` of complex numbers `std::complex<T>` on device, requested by Tensorflow folks. Technically `std::complex` is not supported by NVCC on device (though it is by clang), so the default `sqrt(std::complex<T>)` function only works on the host. Here we create an overload to add back the functionality. Also modified the CMake file to add `--relaxed-constexpr` (or equivalent) flag for NVCC to allow calling constexpr functions from device functions, and added support for specifying compute architecture for NVCC (was already available for clang).
* Fix missing EIGEN_DEVICE_FUNCGravatar rgreenblatt2020-12-20
|
* * Add iterative psqrt<double> for AVX and SSE when FMA is available. This ↵Gravatar Rasmus Munk Larsen2020-12-16
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | provides a ~10% speedup. * Write iterative sqrt explicitly in terms of pmadd. This gives up to 7% speedup for psqrt<float> with AVX & SSE with FMA. * Remove iterative psqrt<double> for NEON, because the initial rsqrt apprimation is not accurate enough for convergence in 2 Newton-Raphson steps and with 3 steps, just calling the builtin sqrt insn is faster. The following benchmarks were compiled with clang "-O2 -fast-math -mfma" and with and without -mavx. AVX+FMA (float) name old cpu/op new cpu/op delta BM_eigen_sqrt_float/1 1.08ns ± 0% 1.09ns ± 1% ~ BM_eigen_sqrt_float/8 2.07ns ± 0% 2.08ns ± 1% ~ BM_eigen_sqrt_float/64 12.4ns ± 0% 12.4ns ± 1% ~ BM_eigen_sqrt_float/512 95.7ns ± 0% 95.5ns ± 0% ~ BM_eigen_sqrt_float/4k 776ns ± 0% 763ns ± 0% -1.67% BM_eigen_sqrt_float/32k 6.57µs ± 1% 6.13µs ± 0% -6.69% BM_eigen_sqrt_float/256k 83.7µs ± 3% 83.3µs ± 2% ~ BM_eigen_sqrt_float/1M 335µs ± 2% 332µs ± 2% ~ SSE+FMA (float) name old cpu/op new cpu/op delta BM_eigen_sqrt_float/1 1.08ns ± 0% 1.09ns ± 0% ~ BM_eigen_sqrt_float/8 2.07ns ± 0% 2.06ns ± 0% ~ BM_eigen_sqrt_float/64 12.4ns ± 0% 12.4ns ± 1% ~ BM_eigen_sqrt_float/512 95.7ns ± 0% 96.3ns ± 4% ~ BM_eigen_sqrt_float/4k 774ns ± 0% 763ns ± 0% -1.50% BM_eigen_sqrt_float/32k 6.58µs ± 2% 6.11µs ± 0% -7.06% BM_eigen_sqrt_float/256k 82.7µs ± 1% 82.6µs ± 1% ~ BM_eigen_sqrt_float/1M 330µs ± 1% 329µs ± 2% ~ SSE+FMA (double) BM_eigen_sqrt_double/1 1.63ns ± 0% 1.63ns ± 0% ~ BM_eigen_sqrt_double/8 6.51ns ± 0% 6.08ns ± 0% -6.68% BM_eigen_sqrt_double/64 52.1ns ± 0% 46.5ns ± 1% -10.65% BM_eigen_sqrt_double/512 417ns ± 0% 374ns ± 1% -10.29% BM_eigen_sqrt_double/4k 3.33µs ± 0% 2.97µs ± 1% -11.00% BM_eigen_sqrt_double/32k 26.7µs ± 0% 23.7µs ± 0% -11.07% BM_eigen_sqrt_double/256k 213µs ± 0% 206µs ± 1% -3.31% BM_eigen_sqrt_double/1M 862µs ± 0% 870µs ± 2% +0.96% AVX+FMA (double) name old cpu/op new cpu/op delta BM_eigen_sqrt_double/1 1.63ns ± 0% 1.63ns ± 0% ~ BM_eigen_sqrt_double/8 6.51ns ± 0% 6.06ns ± 0% -6.95% BM_eigen_sqrt_double/64 52.1ns ± 0% 46.5ns ± 1% -10.80% BM_eigen_sqrt_double/512 417ns ± 0% 373ns ± 1% -10.59% BM_eigen_sqrt_double/4k 3.33µs ± 0% 2.97µs ± 1% -10.79% BM_eigen_sqrt_double/32k 26.7µs ± 0% 23.8µs ± 0% -10.94% BM_eigen_sqrt_double/256k 214µs ± 0% 208µs ± 2% -2.76% BM_eigen_sqrt_double/1M 866µs ± 3% 923µs ± 7% ~
* Add an additional step of Newton-Raphson for `psqrt<double>` on Arm, which ↵Gravatar Rasmus Munk Larsen2020-12-15
| | | | otherwise has an error of ~1000 ulps.
* Remove comma at the end of enumeration list to silence C++03 warningsGravatar David Tellenbach2020-12-13
|
* Fix implicit cast to double.Gravatar Antonio Sanchez2020-12-12
| | | | | Triggers `-Wimplicit-float-conversion`, causing a bunch of build errors in Google due to `-Wall`.
* Fix NEON pmax<PropagateNumbers,Packet4bf>.Gravatar Antonio Sanchez2020-12-11
| | | | Simple typo, the max impl called pmin instead of pmax for floats.
* Fix typo in AVX512 packet math.Gravatar Antonio Sanchez2020-12-11
|
* Remove unused macro in Half.hGravatar David Tellenbach2020-12-12
|
* Fix more SSE/AVX packet conversions for peven.Gravatar Antonio Sanchez2020-12-11
| | | | MSVC doesn't like function-style casts and forces us to use intrinsics.
generated by cgit v1.2.3 (git 2.39.1) at 2024-06-13 22:49:00 +0000