cuda shared memory between blocks cuda shared memory between blocks

This chapter contains a summary of the recommendations for optimization that are explained in this document. Shared memory banks are organized such that successive 32-bit words are assigned to successive banks and the bandwidth is 32 bits per bank per clock cycle. Once we have located a hotspot in our applications profile assessment and determined that custom code is the best approach, we can use CUDA C++ to expose the parallelism in that portion of our code as a CUDA kernel. The host runtime component of the CUDA software environment can be used only by host functions. Sample CUDA configuration data reported by deviceQuery. Hence, the A100 GPU enables a single thread block to address up to 163 KB of shared memory and GPUs with compute capability 8.6 can address up to 99 KB of shared memory in a single thread block. By reversing the array using shared memory we are able to have all global memory reads and writes performed with unit stride, achieving full coalescing on any CUDA GPU. So threads must wait approximatly 4 cycles before using an arithmetic result. The peak theoretical bandwidth between the device memory and the GPU is much higher (898 GB/s on the NVIDIA Tesla V100, for example) than the peak theoretical bandwidth between host memory and device memory (16 GB/s on the PCIe x16 Gen3). The number of elements is multiplied by the size of each element (4 bytes for a float), multiplied by 2 (because of the read and write), divided by 109 (or 1,0243) to obtain GB of memory transferred. The CUDA Driver API thus is binary-compatible (the OS loader can pick up a newer version and the application continues to work) but not source-compatible (rebuilding your application against a newer SDK might require source changes). Since you don't indicate where your "locally sorted" data resides, this could indicate a copying of that much data at least (for example, if they are locally sorted and reside in shared memory). Both of your questions imply some sort of global synchronization. Indicate whether compute processes can run on the GPU and whether they run exclusively or concurrently with other compute processes. For exponentiation using base 2 or 10, use the functions exp2() or expf2() and exp10() or expf10() rather than the functions pow() or powf(). APIs can be deprecated and removed. For example, a matrix multiplication of the same matrices requires N3 operations (multiply-add), so the ratio of operations to elements transferred is O(N), in which case the larger the matrix the greater the performance benefit. There are many such factors involved in selecting block size, and inevitably some experimentation is required. On discrete GPUs, mapped pinned memory is advantageous only in certain cases. Optimizing memory usage starts with minimizing data transfers between the host and the device because those transfers have much lower bandwidth than internal device data transfers. The multidimensional aspect of these parameters allows easier mapping of multidimensional problems to CUDA and does not play a role in performance. Static linking makes the executable slightly larger, but it ensures that the correct version of runtime library functions are included in the application binary without requiring separate redistribution of the CUDA Runtime library. Any CPU timer can be used to measure the elapsed time of a CUDA call or kernel execution. These transfers are costly in terms of performance and should be minimized. (The exceptions to this are kernel launches, which return void, and cudaGetErrorString(), which returns a character string describing the cudaError_t code that was passed into it.) Using asynchronous copies does not use any intermediate register. CUDA provides a simple barrier synchronization primitive, __syncthreads(). These examples assume compute capability 6.0 or higher and that accesses are for 4-byte words, unless otherwise noted. I think this pretty much implies that you are going to have the place the heads of each queue in global memory. On the other hand, some applications designs will require some amount of refactoring to expose their inherent parallelism. Excessive use can reduce overall system performance because pinned memory is a scarce resource, but how much is too much is difficult to know in advance. Higher occupancy does not always equate to higher performance-there is a point above which additional occupancy does not improve performance. Data Transfer Between Host and Device, 9.1.2. In A copy kernel that illustrates misaligned accesses, data is copied from the input array idata to the output array, both of which exist in global memory. Page-locked or pinned memory transfers attain the highest bandwidth between the host and the device. The various principal traits of the memory types are shown in Table 1. Cached in L1 and L2 by default on devices of compute capability 6.0 and 7.x; cached only in L2 by default on devices of lower compute capabilities, though some allow opt-in to caching in L1 as well via compilation flags. One of the key differences is the fused multiply-add (FMA) instruction, which combines multiply-add operations into a single instruction execution. The results of these optimizations are summarized in Table 3. Overall, best performance is achieved when using asynchronous copies with an element of size 8 or 16 bytes. The following complete code (available on GitHub) illustrates various methods of using shared memory. Data should be kept on the device as long as possible. Medium Priority: To hide latency arising from register dependencies, maintain sufficient numbers of active threads per multiprocessor (i.e., sufficient occupancy). Users wishing to take advantage of such a feature should query its availability with a dynamic check in the code: Alternatively the applications interface might not work at all without a new CUDA driver and then its best to return an error right away: A new error code is added to indicate that the functionality is missing from the driver you are running against: cudaErrorCallRequiresNewerDriver. Some recent Linux distributions enable automatic NUMA balancing (or AutoNUMA) by default. In CUDA there is no defined global synchronization mechanism except the kernel launch. Whether a device has this capability is indicated by the asyncEngineCount field of the cudaDeviceProp structure (or listed in the output of the deviceQuery CUDA Sample). NVIDIA accepts no liability for inclusion and/or use of NVIDIA products in such equipment or applications and therefore such inclusion and/or use is at customers own risk. CUDA reserves 1 KB of shared memory per thread block. It is recommended to use cudaMallocAsync() and cudaFreeAsync() which are stream ordered pool allocators to manage device memory. Even though such an access requires only 1 transaction on devices of compute capability 2.0 or higher, there is wasted bandwidth in the transaction, because only one 4-byte word out of 8 words in a 32-byte cache segment is used. Performance Improvements Optimizing C = AA, Comparing Synchronous vs Asynchronous Copy from Global Memory to Shared Memory, Comparing Performance of Synchronous vs Asynchronous Copy from Global Memory to Shared Memory, Table 4. A stream is simply a sequence of operations that are performed in order on the device. Shared memory can also be used to avoid uncoalesced memory accesses by loading and storing data in a coalesced pattern from global memory and then reordering it in shared memory. The details of various CPU timing approaches are outside the scope of this document, but developers should always be aware of the resolution their timing calls provide. Where to Install Redistributed CUDA Libraries, 17.4. Strong scaling is a measure of how, for a fixed overall problem size, the time to solution decreases as more processors are added to a system. Weak scaling is often equated with Gustafsons Law, which states that in practice, the problem size scales with the number of processors. The way to avoid strided access is to use shared memory as before, except in this case a warp reads a row of A into a column of a shared memory tile, as shown in An optimized handling of strided accesses using coalesced reads from global memory. A stride of 2 results in a 50% of load/store efficiency since half the elements in the transaction are not used and represent wasted bandwidth. For more information on the Runtime API, refer to CUDA Runtime of the CUDA C++ Programming Guide. Providing the two argument version of __launch_bounds__(maxThreadsPerBlock,minBlocksPerMultiprocessor) can improve performance in some cases. It is easy and informative to explore the ramifications of misaligned accesses using a simple copy kernel, such as the one in A copy kernel that illustrates misaligned accesses. At a minimum, you would need some sort of selection process that can access the heads of each queue. The application should also maximize parallel execution at a higher level by explicitly exposing concurrent execution on the device through streams, as well as maximizing concurrent execution between the host and the device. However, this latency can be completely hidden by the execution of threads in other warps. Alternatively, NVRTC can generate cubins directly starting with CUDA 11.1. Figure 6 illustrates such a situation; in this case, threads within a warp access words in memory with a stride of 2. Having completed the GPU acceleration of one or more components of the application it is possible to compare the outcome with the original expectation. What if you need multiple dynamically sized arrays in a single kernel? The constant memory space is cached. Note that the timings are measured on the GPU clock, so the timing resolution is operating-system-independent. The cudaGetDeviceProperties() function reports various features of the available devices, including the CUDA Compute Capability of the device (see also the Compute Capabilities section of the CUDA C++ Programming Guide). NVIDIA accepts no liability for inclusion and/or use of NVIDIA products in such equipment or applications and therefore such inclusion and/or use is at customers own risk. The NVIDIA Ampere GPU architecture increases the capacity of the L2 cache to 40 MB in Tesla A100, which is 7x larger than Tesla V100. For some fractional exponents, exponentiation can be accelerated significantly compared to the use of pow() by using square roots, cube roots, and their inverses. Many codes accomplish a significant portion of the work with a relatively small amount of code. It is customers sole responsibility to evaluate and determine the applicability of any information contained in this document, ensure the product is suitable and fit for the application planned by customer, and perform the necessary testing for the application in order to avoid a default of the application or the product. This is of particular note with loop counters: since it is common for loop counters to have values that are always positive, it may be tempting to declare the counters as unsigned. A more robust approach is to selectively introduce calls to fast intrinsic functions only if merited by performance gains and where altered behavior can be tolerated. Is it possible to create a concave light? This illustrates the use of the shared memory as a user-managed cache when the hardware L1 cache eviction policy does not match up well with the needs of the application or when L1 cache is not used for reads from global memory. likewise return their own sets of error codes. Timeline comparison for copy and kernel execution, Table 1. These copy instructions are asynchronous, with respect to computation and allow users to explicitly control overlap of compute with data movement from global memory into the SM. This is the default if using nvcc to link in CUDA 5.5 and later. In this guide, they represent a typical case. If static linking against the CUDA Runtime is impractical for some reason, then a dynamically-linked version of the CUDA Runtime library is also available. They produce equivalent results. The __pipeline_wait_prior(0) will wait until all the instructions in the pipe object have been executed. Similarly, the single-precision functions sinpif(), cospif(), and sincospif() should replace calls to sinf(), cosf(), and sincosf() when the function argument is of the form *. Actions that present substantial improvements for most CUDA applications have the highest priority, while small optimizations that affect only very specific situations are given a lower priority. In many cases, the amount of shared memory required by a kernel is related to the block size that was chosen, but the mapping of threads to shared memory elements does not need to be one-to-one. Using shared memory to improve the global memory load efficiency in matrix multiplication. High Priority: To maximize developer productivity, profile the application to determine hotspots and bottlenecks. Non-default streams (streams other than stream 0) are required for concurrent execution because kernel calls that use the default stream begin only after all preceding calls on the device (in any stream) have completed, and no operation on the device (in any stream) commences until they are finished. The first is the compute capability, and the second is the version number of the CUDA Runtime and CUDA Driver APIs. To keep the kernels simple, M and N are multiples of 32, since the warp size (w) is 32 for current devices. The cudaDeviceCanAccessPeer() can be used to determine if peer access is possible between any pair of GPUs. When using a shared or static library, follow the release notes of said library to determine if the library supports minor version compatibility. CUDA Toolkit and Minimum Driver Versions. Useful Features for tex1D(), tex2D(), and tex3D() Fetches, __launch_bounds__(maxThreadsPerBlock,minBlocksPerMultiprocessor), Using the CUDA Occupancy Calculator to project GPU multiprocessor occupancy, cudaOccupancyMaxActiveBlocksPerMultiprocessor, // When the program/library launches work, // When the program/library is finished with the context, Table 5. On the other hand, some applications designs will require some amount of refactoring to expose their inherent parallelism. More difficult to parallelize are applications with a very flat profile - i.e., applications where the time spent is spread out relatively evenly across a wide portion of the code base. To maintain architectural compatibility, static shared memory allocations remain limited to 48 KB, and an explicit opt-in is also required to enable dynamic allocations above this limit. Higher compute capability versions are supersets of lower (that is, earlier) versions, so they are backward compatible. Functions following functionName() naming convention are slower but have higher accuracy (e.g., sinf(x) and expf(x)). Optimal global memory coalescing is achieved for both reads and writes because global memory is always accessed through the linear, aligned index t. The reversed index tr is only used to access shared memory, which does not have the sequential access restrictions of global memory for optimal performance. The warp wide reduction operations support arithmetic add, min, and max operations on 32-bit signed and unsigned integers and bitwise and, or and xor operations on 32-bit unsigned integers. A further improvement can be made to how Using shared memory to improve the global memory load efficiency in matrix multiplication deals with matrix B. GPUs with compute capability 8.6 support shared memory capacity of 0, 8, 16, 32, 64 or 100 KB per SM. Parallelizing these functions as well should increase our speedup potential. The details of managing the accelerator device are handled implicitly by an OpenACC-enabled compiler and runtime. When our CUDA 11.1 application (i.e. The warp size is 32 threads and the number of banks is also 32, so bank conflicts can occur between any threads in the warp. There are many possible approaches to profiling the code, but in all cases the objective is the same: to identify the function or functions in which the application is spending most of its execution time. To analyze performance, it is necessary to consider how warps access global memory in the for loop.