本文探讨了GPU编程在并行计算中的应用及性能提升的关键因素。文章指出，虽然GPU能够显著加速计算任务，但其效果往往被夸大的原因是与之对比的CPU程序通常未进行优化。此外，为了最大化利用GPU资源，需要成千上万的线程同时运行，并且必须妥善处理内存延迟问题。在许多情况下，全局内存带宽成为限制因素，因此采用有效的内存访问策略对于提高性能至关重要。
1. Someone once said that if you don't care much about the performance, parallel programming is easy.
2. Many of the performance improvements are published, giving the impression that using GPU programming has a result of more than hundreds time faster. Most of times, the compared CPU programs are totally non-optimized and possibly poorly designed for CPU computation.
3. GPU typically needs thousands of threads for full utilization. This is not only important to achieve a high computed rate, but more to hide the memory latencies.
4. Due to the fact that the global memory bandwidth is the limiting factor in many cases. Coalrscing techniques can dramatically influence performance if global memory is used heavily.
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