fma

Tensorflow 运行警告提示 Your CPU supports instructions that this TensorFlow binary was not compiled...

天涯浪子 提交于 2021-02-15 03:55:50
由于现在神经网络这个东西比较火,准确的说是深度学习这个东西比较火,我们实验室准备靠这个东西发几个CCF A类的文章,虽然我不太懂这东西,兴趣也一般都是毕竟要跟随主流的,于是今天安装起了 Tensorflow 这个深度学习的框架。 安装好以后运行一个Demo ,如下: import tensorflow as tf a =tf.constant(2 ) b =tf.constant(20 ) with tf.Session() as sess: print (sess.run(a*b)) 运行结果如下: 2018-05-03 19:57:44.151803: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA 2018-05-03 19:57:44.251905: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:892] successful NUMA node read from SysFS had negative value (-1), but

使用grep搜索多个字符串

徘徊边缘 提交于 2020-10-24 14:32:48
grep是一个强大的 命令 行工具,它允许在一个或多个文件中搜索与正则表达式匹配的行,并将匹配的结果写入标准输出。 Grep的多条件搜索模式 grep支持三种正则表达式语法:Basic、Extended和Perl正则表达式。当没有指定正则表达式类型时,grep将搜索模式解释为Basic基本正则表达式。 使用多条件搜索模式时,请使用 | 管道符。使用grep的基本正则表达式搜索多个条件,语法如下: ]# grep 'pattern1\|pattern2' file 当使用基本正则表达式时,需要使用 \ 转义符为 | 管道符转义。 如果使用扩展模式,可以添加 -E 参数。使用扩展模式,就不需要为 | 管道符添加转义符了。也可以使用 egrep 命令 ,这个命令和 grep -E 用法一样。 ]# grep 'pattern1|pattern2' file ]# egrep 'pattern1|pattern2' file 使用实例 检查操作系统是否开启虚拟化功能,使用Basic基本模式: [root@localhost ~]# grep 'vmx\|svm' /proc/cpuinfo flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse

首台获得TOP500榜首的ARM架构超算——富岳Fugaku

微笑、不失礼 提交于 2020-08-08 18:02:59
  文|乌镇智库   最近发布的TOP500榜单中,日本的高性能计算系统Fugaku(富岳)以415.53 PFlop/s的Linpack性能拔得头筹(使用152,064个节点),为第二名美国超算Summit的2.8倍。          此外在多项超级计算机基准测试中,Fugaku也名列前茅 :在HPCG测试中,它使用 138,240个 节点获得了 13.366 PFlop/s 的算力,而在HPL-AI测试中,它使用 126,720个节点 获得了 1.421 EFlop/s 的算力。 Fugaku采用富士通的ARM架构A64FX芯片,是第一个获得TOP500榜首的基于ARM的高性能计算系统。          Fugaku    “京”的后继机    Fugaku富岳 :富岳是日本富士山的别称,借寓富士山海拔及山脚广阔馥郁的平原,以呈现Fugaku卓越的性能和庞大的用户群体。   01    Fugaku诞生历程   作为超级计算机“京(Kei,K Computer)”的后继产品,Fugaku的诞生还要从K Computer说起。虽然日本1980年代末期的第五代计算机项目失败了,但建造最快计算机的雄心从未泯灭。   自2006年以来,日本理化学研究所(RIKEN)和富士通共同开发了K Computer,旨在2012年开始公共服务。 2011年6月,K Computer凭借8

AVX2: Computing dot product of 512 float arrays

☆樱花仙子☆ 提交于 2020-07-14 17:43:31
问题 I will preface this by saying that I am a complete beginner at SIMD intrinsics. Essentially, I have a CPU which supports the AVX2 instrinsic ( Intel(R) Core(TM) i5-7500T CPU @ 2.70GHz ). I would like to know the fastest way to compute the dot product of two std::vector<float> of size 512 . I have done some digging online and found this and this, and this stack overflow question suggests using the following function __m256 _mm256_dp_ps(__m256 m1, __m256 m2, const int mask); , However, these

AVX2: Computing dot product of 512 float arrays

佐手、 提交于 2020-07-14 17:42:48
问题 I will preface this by saying that I am a complete beginner at SIMD intrinsics. Essentially, I have a CPU which supports the AVX2 instrinsic ( Intel(R) Core(TM) i5-7500T CPU @ 2.70GHz ). I would like to know the fastest way to compute the dot product of two std::vector<float> of size 512 . I have done some digging online and found this and this, and this stack overflow question suggests using the following function __m256 _mm256_dp_ps(__m256 m1, __m256 m2, const int mask); , However, these

kvm常用操作

二次信任 提交于 2020-04-27 19:21:18
kvm常用操作 # 创建qcow2文件 qemu-img create -f qcow2 demo.qcow2 200G ​ # 挂载盘 attach-disk gz-demo /datafs2/vm/gz-demo/demo.qcow2 vdc --subdriver qcow2 --config ​ #修改内存大小 virsh setmaxmem 64G demo --config ​ #修改cpu核数 virsh setvcpus demo 8 --config ​ #重启 virsh reboot demo ​ #导出虚拟机配置文件 virsh dumpxml demo > demo.xml ​ #导入虚拟机配置文件 virsh define demo.xml ​ 虚拟机停机迁移步骤 从原宿主导出虚拟机配置文件. virsh dumpxml demo > demo.xml 目标宿主导入虚拟机配置文件 virsh define demo.xml 关闭原虚拟机,拷贝原有路径虚拟机文件,如果目录有变更,需要编辑修改磁盘文件路径 virsh shutdown demo scp demo ${detination}:/data/ 出现CPU不支持报错 error: unsupported configuration: guest and host CPU are not

Tensorflow 运行警告提示 Your CPU supports instructions that this TensorFlow binary was not compiled to use

放肆的年华 提交于 2020-04-09 05:35:28
由于现在神经网络这个东西比较火,准确的说是深度学习这个东西比较火,我们实验室准备靠这个东西发几个CCF A类的文章,虽然我不太懂这东西,兴趣也一般都是毕竟要跟随主流的,于是今天安装起了 Tensorflow 这个深度学习的框架。 安装好以后运行一个Demo ,如下: import tensorflow as tf a =tf.constant(2 ) b =tf.constant(20 ) with tf.Session() as sess: print (sess.run(a*b)) 运行结果如下: 2018-05-03 19:57:44.151803: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA 2018-05-03 19:57:44.251905: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:892] successful NUMA node read from SysFS had negative value (-1), but

How to solve “illegal instruction” for vfmadd213ps?

限于喜欢 提交于 2020-01-30 12:26:09
问题 I have tried AVX intrinsics. But it caused "Unhandled exception at 0x00E01555 in test.exe: 0xC000001D: Illegal Instruction." I used Visual studio 2015. And the exception error is caused at "vfmadd213ps ymm2,ymm1,ymm0" instruction. I have tried set "/arch:AVX" and "/arch:AVX2", but still error caused. Below is my code. #include <immintrin.h> int main(int argc, char *argv[]) { float a[8] = { 0 }; float b[8] = { 0 }; float c[8] = { 0 }; __m256 _a = _mm256_loadu_ps(a); __m256 _b = _mm256_loadu_ps

Can I use the AVX FMA units to do bit-exact 52 bit integer multiplications?

妖精的绣舞 提交于 2019-12-28 05:59:08
问题 AXV2 doesn't have any integer multiplications with sources larger than 32-bit. It does offer 32 x 32 -> 32 multiplies, as well as 32 x 32 -> 64 multiplies 1 , but nothing with 64-bit sources. Let's say I need an unsigned multiply with inputs larger than 32-bit, but less or equal to 52-bits - can I simply use the floating point DP multiply or FMA instructions, and will the output be bit-exact when the integer inputs and results can be represented in 52 or fewer bits (i.e., in the range [0, 2

How to avoid the error of AVX2 when the matrix dimension isn't multiples of 4?

一个人想着一个人 提交于 2019-12-24 22:34:28
问题 I made matrix-vector multiplication program using AVX2, FMA in C. I compiled using GCC ver7 with -mfma, -mavx. However, I got the error "incorrect checksum for freed object - object was probably modified after being freed." I think the error would generate if the matrix dimension isn't multiples of 4. I know AVX2 use ymm register that can use 4 double precision floating point number. Therefore, I can use AVX2 without error in case the matrix is multiples of 4. But, here is my question. How