月份：2016年12月

这篇笔记摘自Professional CUDA C Programming：

Global memory loads/stores are staged through caches, as shown in Figure 4-6. Global memory is a logical memory space that you can access from your kernel. All application data initially resides in DRAM, the physical device memory. Kernel memory requests are typically served between the device DRAM and SM on-chip memory using either 128-byte or 32-byte memory transactions.

All accesses to global memory go through the L2 cache. Many accesses also pass through the L1 cache, depending on the type of access and your GPU’s architecture. If both L1 and L2 caches are used, a memory access is serviced by a 128-byte memory transaction. If only the L2 cache is used, a memory access is serviced by a 32-byte memory transaction. On architectures that allow the L1 cache to be used for global memory caching, the L1 cache can be explicitly enabled or disabled at compile time.

An L1 cache line is 128 bytes, and it maps to a 128-byte aligned segment in device memory. If each thread in a warp requests one 4-byte value, that results in 128 bytes of data per request, which maps perfectly to the cache line size and device memory segment size.

There are two characteristics of device memory accesses that you should strive for when optimizing your application:
➤ Aligned memory accesses
➤ Coalesced memory

Memory store operations are relatively simple. The L1 cache is not used for store operations on either Fermi or Kepler GPUs, store operations are only cached in the L2 cache before being sent to device memory. Stores are performed at a 32-byte segment granularity. Memory transactions can be one, two, or four segments at a time. For example, if two addresses fall within the same 128-byte region but not within an aligned 64-byte region, one four-segment transaction will be issued (that is, issuing a single four-segment transaction performs better than issuing two one-segment transactions).

这篇笔记摘自Professional CUDA C Programming：

In general, the host cannot directly access device variables, and the device cannot directly access host variables. There is one exception to this rule: zero-copy memory. Both the host and device can access zero-copy memory.

GPU threads can directly access zero-copy memory. There are several advantages to using zero-copy memory in CUDA kernels, such as:
➤ Leveraging host memory when there is insufficient device memory
➤ Avoiding explicit data transfer between the host and device
➤ Improving PCIe transfer rates
When using zero-copy memory to share data between the host and device, you must synchronize memory accesses across the host and device. Modifying data in zero-copy memory from both the host and device at the same time will result in undefned behavior.

There are two common categories of heterogeneous computing system architectures: Integrated and discrete.

In integrated architectures, CPUs and GPUs are fused onto a single die and physically share main memory. In this architecture, zero-copy memory is more likely to benefit both performance and programmability because no copies over the PCIe bus are necessary.

For discrete systems with devices connected to the host via PCIe bus, zero-copy memory is advantageous only in special cases.

Because the mapped pinned memory is shared between the host and device, you must synchronize memory accesses to avoid any potential data hazards caused by multiple threads accessing the same memory location without synchronization.

Be careful to not overuse zero-copy memory. Device kernels that read from zero-copy memory can be very slow due to its high-latency.

这篇笔记摘自Professional CUDA C Programming：

Allocated host memory is by default pageable, that is, subject to page fault operations that move data in host virtual memory to different physical locations as directed by the operating system. Virtual memory offers the illusion of much more main memory than is physically available, just as the L1 cache offers the illusion of much more on-chip memory than is physically available.

The GPU cannot safely access data in pageable host memory because it has no control over when the host operating system may choose to physically move that data. When transferring data from pageable host memory to device memory, the CUDA driver first allocates temporary page-locked or pinned host memory, copies the source host data to pinned memory, and then transfers the data from pinned memory to device memory, as illustrated on the left side of Figure 4-4.

The CUDA runtime allows you to directly allocate pinned host memory using:
cudaError_t cudaMallocHost(void **devPtr, size_t count);
This function allocates count bytes of host memory that is page-locked and accessible to the device. Since the pinned memory can be accessed directly by the device, it can be read and written with much higher bandwidth than pageable memory. However, allocating excessive amounts of pinned memory might degrade host system performance, since it reduces the amount of pageable memory available to the host system for storing virtual memory data.

这篇笔记摘自Professional CUDA C Programming：

Warps are the basic unit of execution in an SM. When you launch a grid of thread blocks, the thread blocks in the grid are distributed among SMs. Once a thread block is scheduled to an SM, threads in the thread block are further partitioned into warps. A warp consists of 32 consecutive threads and all threads in a warp are executed in Single Instruction Multiple Thread (SIMT) fashion; that is, all threads execute the same instruction, and each thread carries out that operation on its own private data. The following figure illustrates the relationship between the logical view and hardware view of a thread block.

A thread block is called an active block when compute resources, such as registers and shared memory, have been allocated to it. The warps it contains are called active warps. Active warps can be further classifed into the following three types:
➤ Selected warp
➤ Stalled warp
➤ Eligible warp
The warp schedulers on an SM select active warps on every cycle and dispatch them to execution units. A warp that is actively executing is called a selected warp. If an active warp is ready for execution but not currently executing, it is an eligible warp. If a warp is not ready for execution, it is a stalled warp. A warp is eligible for execution if both of the following two conditions is met:
➤ Thirty-two CUDA cores are available for execution.
➤ All arguments to the current instruction are ready.

GUIDELINES FOR GRID AND BLOCK SIZE
Using these guidelines will help your application scale on current and future devices:
➤ Keep the number of threads per block a multiple of warp size (32).
➤ Avoid small block sizes: Start with at least 128 or 256 threads per block.
➤ Adjust block size up or down according to kernel resource requirements.
➤ Keep the number of blocks much greater than the number of SMs to expose sufficient parallelism to your device.
➤ Conduct experiments to discover the best execution configuration and resource usage.

这篇笔记摘自Professional CUDA C Programming：

The GPU architecture is built around a scalable array of Streaming Multiprocessors (SM). GPU hardware parallelism is achieved through the replication of this architectural buildin block.
Each SM in a GPU is designed to support concurrent execution of hundreds of threads, and there are generally multiple SMs per GPU, so it is possible to have thousands of threads executing concurrently on a single GPU. When a kernel grid is launched, the thread blocks of that kernel grid are distributed among available SMs for execution. Once scheduled on an SM, the threads of a thread block execute concurrently only on that assigned SM. Multiple thread blocks may be assigned to the same SM at once and are scheduled based on the availability of SM resources. Instructions within a single thread are pipelined to leverage instruction-level parallelism, in addition to the thread-level parallelism you are already familiar with in CUDA. 。

一个GPU包含多个Streaming Multiprocessor，而每个Streaming Multiprocessor又包含多个core。Streaming Multiprocessors支持并发执行多个thread。

A thread block is scheduled on only one SM. Once a thread block is scheduled on an SM, it remains there until execution completes. An SM can hold more than one thread block at the same time. The following figure illustrates the corresponding components from the logical view and hardware view of CUDA programming:

一个block只能调度到一个Streaming Multiprocessor上运行。一个Streaming Multiprocessor可以同时运行多个block。