CUDA C Programming Guide 在线教程学习笔记 Part 13

▶ 纹理内存访问补充（见纹理内存博客 http://www.cnblogs.com/cuancuancuanhao/p/7809713.html）

▶ 计算能力

● 不同计算能力的硬件对计算特性的支持。

　　

● 不同计算能力的硬件技术特性（重要）。

　　

● 浮点运算技术标准描述（原文）

■ All compute devices follow the IEEE 754-2008 standard for binary floating-point arithmetic with the following deviations:

　　There is no dynamically configurable rounding mode; however, most of the operations support multiple IEEE rounding modes, exposed via device intrinsics;

　　There is no mechanism for detecting that a floating-point exception has occurred and all operations behave as if the IEEE-754 exceptions are always masked, and deliver the masked response as defined by IEEE-754 if there is an exceptional event; for the same reason, while SNaN encodings are supported, they are not signaling and are handled as quiet;

　　The result of a single-precision floating-point operation involving one or more input NaNs is the quiet NaN of bit pattern 0x7fffffff;

　　Double-precision floating-point absolute value and negation are not compliant with IEEE-754 with respect to NaNs; these are passed through unchanged;

Code must be compiled with -ftz=false, -prec-div=true, and -prec-sqrt=true to ensure IEEE compliance (this is the default setting; see the nvcc user manual for description of these compilation flags).

Regardless of the setting of the compiler flag -ftz,

    Atomic single-precision floating-point adds on global memory always operate in flush-to-zero mode, i.e., behave equivalent to FADD.F32.FTZ.RN,
    Atomic single-precision floating-point adds on shared memory always operate with denormal support, i.e., behave equivalent to FADD.F32.RN.

■ IEEE-754R 标准，CUDA 设备上函数 fminf()，fmin()，fmaxf()，fmax() 在有且仅有一个参数为NaN时，返回值将是另一个参数。

■ 浮点数转化为整数，且超出整形格式边界时，IEEE-754 称为未定义，而 CUDA 设备将该数映射到整形格式边界（改格式能表示的最大或最小的数）。

■ 整数除以零或整数溢出时，IEEE-754 称为未定义，而 CUDA 设备将抛出一个非特定的值。

 

● 各代 CC 设备的新特性介绍

■ 调整共享内存和 L1 缓存的平衡

// driver_types.h
enum __device_builtin__ cudaFuncCache
{
    cudaFuncCachePreferNone = 0,    // 默认缓存模式
    cudaFuncCachePreferShared = 1,  // 扩大共享内存缩小 L1 缓存
    cudaFuncCachePreferL1 = 2,      // 扩大 L1 缓存缩小共享内存
    cudaFuncCachePreferEqual = 3    // 等量大小的共享内存和 L1 缓存
};

// cuda_runtime_api.h
extern __host__ cudaError_t CUDARTAPI cudaDeviceSetCacheConfig(enum cudaFuncCache cacheConfig);