Introduction Theoretical Peak GB/s 800 ◆-GeForce GPU 700 -◆-Tesla GPU ◆-Intel CPU 600 500 400 300 200 100 0 2003 2005 2007 2009 2011 2013 2015 Figure 2 Memory Bandwidth for the CPU and GPU The reason behind the discrepancy in floating-point capability between the CPU and the GPU is that the GPU is specialized for compute-intensive,highly parallel computation -exactly what graphics rendering is about-and therefore designed such that more transistors are devoted to data processing rather than data caching and flow control,as schematically illustrated by Figure 3. Control ALU ALU ALU ALU Cache DRAM DRAM CPU GPU Figure 3 The GPU Devotes More Transistors to Data Processing More specifically,the GPU is especially well-suited to address problems that can be expressed as data-parallel computations-the same program is executed on many data elements in parallel-with high arithmetic intensity-the ratio of arithmetic operations to memory operations.Because the same program is executed for each data element, www.nvidia.com CUDA C Programming Guide PG-02829-001_v8.012Introduction www.nvidia.com CUDA C Programming Guide PG-02829-001_v8.0 | 2 Figure 2 Memory Bandwidth for the CPU and GPU The reason behind the discrepancy in floating-point capability between the CPU and the GPU is that the GPU is specialized for compute-intensive, highly parallel computation - exactly what graphics rendering is about - and therefore designed such that more transistors are devoted to data processing rather than data caching and flow control, as schematically illustrated by Figure 3. Cache Control ALU ALU ALU ALU DRAM CPU DRAM GPU Figure 3 The GPU Devotes More Transistors to Data Processing More specifically, the GPU is especially well-suited to address problems that can be expressed as data-parallel computations - the same program is executed on many data elements in parallel - with high arithmetic intensity - the ratio of arithmetic operations to memory operations. Because the same program is executed for each data element