GPU作业集

1. On a typical CPU, what component uses the greatest percentage oftransistors? (5%)cache2. What is the motivation for pipelining a CPU? (5%)To leverage the instruction-level parallelism and significantly reduced clock period.要充分利用指令级并行性，并显着降低时钟周期。

3. What is the motivation for branch prediction in a CPU? (5%)Avoid stall cost by speculating what might be executed通过预测来避免stall开销4. True or false: A typical CPU can retire more than one instruction percycle. Why or why not? (5%)不对，如果指典型的单流水线CPU，一般CPU在一个时钟周期内只发射一条指令，那不论流水线多长，只能是一个周期完成一条指令。

但如果指使用超标量体系的微处理器设计，通过在CPU上内置多条流水线（增加多条流水线），使CPU在一个时钟周期内可以执行多条指令，以空间换取时间。

可以实现IPC>1。

5. A superscalar CPU and a GPU both duplicate components to exploitparallelism. Compare the type of parallelism in the two designs. (10%)superscalar-> increase pipeline width, Instruction-Level (ILP) extractionGPU-> many slimmed down cores to run in parallel, with many hardware controlled threads.超标量CPU：增加流水线宽度，在指令级并行GPU：许多具有硬件控制线程的精简core并发运行，单个SM（由多个SP核构成）中的多个ALU与超标量流水线有相似的功能，但不同SM可以执行不同的指令流。

6. What is the primary way CPUs hide main memory access latency? What isthe primary way for GPUs? (10%)cpu: cache;GPU: have a lot of independent fragments(warps).Avoid latency stalls by interleaving execution of many groups of fragments CPU使用cache缓存技术，利用空间临近性GPU有很多独立的fragments（warps），利用众多组fragments间零切换开销的交错执行来隐藏延时stall。

7. Why do GPUs lack the large caches found on-chip in CPUs? (10%)Cost and more ALUs occupy more area.Cache的制造成本很高。

大量ALU单元占用了大量面积8. What is a divergent branch? How does the GPU handle it? (20%)In the same fragment(warp), the branches go into different execution path.GPU will execute all paths, and then select the results by different threads.Divergent branch是指在同一个fragment（warp）内，branch进入不同的执行路径。

即在Nvidia公司的GPU中，32线程组成一个warp，SM将一个warp映射到一个SM 上，每个warp中的线程映射到不同SP核上，每个线程有独立的指令地址和寄存器状态，当一个warp中所有的线程具有相同的指令地址，即执行相同的路径时，SIMT的效率最高。

但是，当warp中的线程同时经过数据相关的条件分支时，不同线程可能进入不同的分支路径，此时，warp将串行完成每个分支路径，并禁止不在此分支上的线程执行，当所有分支都执行完毕后，warp中的所有线程汇聚在同一个原先的执行路径上。

因而warp执行时间是所有分支时间总和。

9. Explain how the number of registers and other GPU core componentsaffect the GPUs ability to hide memory latencies. (20%)More registers, more ALUs, bigger context storage will let the GPU handle more fragments, which means higher ability. However, this is really costly.10. Based on lectures of week1 and 2, provide five questions (and answers)you would ask on this homework if you were teaching this course. Use anycombination of question types, e.g., true/false, multiple choice, short answer,essay, coding, etc. Your questions can overlap with questions on this homeworkwithin reason. (10%)（1）SIMD宽度：即SM中SP的个数，SM是一个具备完整指令译码和执行的多路SIMD 处理器。

每一路处理单元就是一个sp，又称为CUDA core。

Fermi为32路（GF100）.warp中线程以SIMD宽度并行执行。

每个SP包括一个整数算数逻辑单元和一个浮点数单元，但是你没有指令取值和译码电路。

SM中32个SP被分成两组，分别由SM中两个调度器控制，每一个为half-warp，运行结果写入一个队列中。

每一时刻可以有两个warp执行，每个SM最多可以保留48个warp数据队列，因此每个SM最多可以有48*32=1536线程交错执行。

又因为每个blcok是分配到一个SM 上，所以一个block内最大线程数就是1536.（2）.简述一下GPU隐藏延时的方法。

GPU采用的是由硬件管理的轻量级线程，可以实现零开销的线程切换。

当有线程应出现等待以后，可以立即切换到另一个处于就绪的线程，用于计算，用计算来隐藏延迟。

当线程计算需要的时间较多，而访问相对较少，延时就可以被计算隐藏，而且线程越多，延时隐藏的更好。

1) Define a grid, a block, and a warp. (10%)Grid : a grid is a group of thread blocks, it can be 1D or 2D, and a grid resides on the device. Block : a block is an array of threads, it can be 1D, 2D or 3D, and the threads in a block resideon the same SM.Warp : threads in a block is further divided in to 32-thread units called warps (the size is Implementation specific, here use 32 for instance).the warp is the unit of thread scheduling in SMs.Grid 就是CPU 调用kernel 函数在GPU 上生成的一个“块”。

一个kernel 一次只能生成有且仅有一个grid。

Block 是线程(thread)的集合，是在一个SM/SMX 上运行的块。

Warp 指的是GPU 执行程序时的调度单位。

2) If there were no upper limit on the number of threads in a block, explain why you would still want to organize your threads into blocks. (20%)A: 1) to limit maximal size of runtime stacks.2) to populate multiple SMs simultaneously因为：（1）All the threads in a block must be launched in the same SM,there canbe certain active blocks in a SM at the same time. So when a blockneeds synchronization or accesses memory. Another block can workusing GPU resource. Using blocks Improve the utilization of resourceand speed up.（2）Threads in a block share(limited)low-latency memory and synchronize execution.（3）在一个block 之内的threads 之间可以互相通过share memory 来通信。

而block 之间的thread 是不能相互通信的。

3) Explain how __syncthreads() should be used when bringing data from global to shared memory for use by all threads in a block. (20%)A: All threads should wait until the shared memory is updated在将全局存储器中数据加载到共享存储器之后调用同步函数__syncthreads()，确保同一块中所有线程都已经完成把全局存储器中数据加载到共享存储器中。