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This can be used to manage data caches, speed up high-performance cooperative parallel algorithms, and facilitate global memory coalescing in cases where it would otherwise not be possible. Per thread resources required by a CUDA kernel might limit the maximum block size in an unwanted way. As a result, all modern processors require parallel code in order to achieve good utilization of their computational power. Some recent Linux distributions enable automatic NUMA balancing (or AutoNUMA) by default. There are a number of tools that can be used to generate the profile. Not requiring driver updates for new CUDA releases can mean that new versions of the software can be made available faster to users. CUDA shared memory not faster than global? This is important for a number of reasons; for example, it allows the user to profit from their investment as early as possible (the speedup may be partial but is still valuable), and it minimizes risk for the developer and the user by providing an evolutionary rather than revolutionary set of changes to the application. Compiler JIT Cache Management Tools, 18.1. This context can be current to as many threads as desired within the creating process, and cuDevicePrimaryCtxRetain will fail if a non-primary context that was created with the CUDA driver API already exists on the device. However, as with APOD as a whole, program optimization is an iterative process (identify an opportunity for optimization, apply and test the optimization, verify the speedup achieved, and repeat), meaning that it is not necessary for a programmer to spend large amounts of time memorizing the bulk of all possible optimization strategies prior to seeing good speedups. Weak scaling is a measure of how the time to solution changes as more processors are added to a system with a fixed problem size per processor; i.e., where the overall problem size increases as the number of processors is increased. 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 *. Conversion to Warp isn't possible for Week 6-7 because there is no support for shared memory or block level synchronization. HBM2 memories, on the other hand, provide dedicated ECC resources, allowing overhead-free ECC protection.2. The cudaChooseDevice() function can be used to select the device that most closely matches a desired set of features. Asynchronous Copy from Global Memory to Shared Memory, 10. ? For best performance, there should be some coherence in memory access by adjacent threads running on the device. The compiler replaces a branch instruction with predicated instructions only if the number of instructions controlled by the branch condition is less than or equal to a certain threshold. Page-locked or pinned memory transfers attain the highest bandwidth between the host and the device. The NVIDIA A100 GPU supports shared memory capacity of 0, 8, 16, 32, 64, 100, 132 or 164 KB per SM. To do this, the simpleMultiply kernel (Unoptimized matrix multiplication) calculates the output elements of a tile of matrix C. In Unoptimized matrix multiplication, a, b, and c are pointers to global memory for the matrices A, B, and C, respectively; blockDim.x, blockDim.y, and TILE_DIM are all equal to w. Each thread in the wxw-thread block calculates one element in a tile of C. row and col are the row and column of the element in C being calculated by a particular thread. 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. Asynchronous copy achieves better performance in nearly all cases. Warp level support for Reduction Operations, 1.4.2.1. This Link TLB has a reach of 64 GB to the remote GPUs memory. For example, the asyncEngineCount field of the device property structure indicates whether overlapping kernel execution and data transfers is possible (and, if so, how many concurrent transfers are possible); likewise, the canMapHostMemory field indicates whether zero-copy data transfers can be performed. Avoid long sequences of diverged execution by threads within the same warp. The ideal scenario is one in which many threads perform a substantial amount of work. To keep the kernels simple, M and N are multiples of 32, since the warp size (w) is 32 for current devices. Then, thread A wants to read Bs element from shared memory, and vice versa. Having identified the hotspots and having done the basic exercises to set goals and expectations, the developer needs to parallelize the code. What sort of strategies would a medieval military use against a fantasy giant? Another, more aggressive, option is -use_fast_math, which coerces every functionName() call to the equivalent __functionName() call. For more information on the Runtime API, refer to CUDA Runtime of the CUDA C++ Programming Guide. Local memory is used only to hold automatic variables. Devices of compute capability 3.x allow a third setting of 32KB shared memory / 32KB L1 cache which can be obtained using the optioncudaFuncCachePreferEqual. Comparing Performance of Synchronous vs Asynchronous Copy from Global Memory to Shared Memory. This chapter examines issues that can affect the correctness of returned data and points to appropriate solutions. This number is divided by the time in seconds to obtain GB/s. To ensure correct results when parallel threads cooperate, we must synchronize the threads. Data Transfer Between Host and Device provides further details, including the measurements of bandwidth between the host and the device versus within the device proper. Lets say that two threads A and B each load a data element from global memory and store it to shared memory. Before I show you how to avoid striding through global memory in the next post, first I need to describe shared memory in some detail. These bindings expose the same features as the C-based interface and also provide backwards compatibility. The remaining portion of this persistent data will be accessed using the streaming property. Shared memory accesses, in counterpoint, are usually worth optimizing only when there exists a high degree of bank conflicts. See the CUDA C++ Programming Guide for further explanations and software requirements for UVA and P2P. These exceptions, which are detailed in Features and Technical Specifications of the CUDA C++ Programming Guide, can lead to results that differ from IEEE 754 values computed on the host system. Devices of compute capability 1.0 to 1.3 have 16 KB/Block, compute 2.0 onwards have 48 KB/Block shared memory by default. Asynchronous transfers enable overlap of data transfers with computation in two different ways. NVIDIA GPU device driver - Kernel-mode driver component for NVIDIA GPUs. If the PTX is also not available, then the kernel launch will fail. Zero copy can be used in place of streams because kernel-originated data transfers automatically overlap kernel execution without the overhead of setting up and determining the optimal number of streams. New APIs can be added in minor versions. To execute any CUDA program, there are three main steps: Copy the input data from host memory to device memory, also known as host-to-device transfer. Sometimes, the best optimization might even be to avoid any data transfer in the first place by simply recomputing the data whenever it is needed. However, if multiple threads requested addresses map to the same memory bank, the accesses are serialized. This ensures your code is compatible. Intermediate data structures should be created in device memory, operated on by the device, and destroyed without ever being mapped by the host or copied to host memory. Furthermore, the need for context switching can reduce utilization when work from several contexts could otherwise execute concurrently (see also Concurrent Kernel Execution). The NVML API is shipped with the CUDA Toolkit (since version 8.0) and is also available standalone on the NVIDIA developer website as part of the GPU Deployment Kit through a single header file accompanied by PDF documentation, stub libraries, and sample applications; see https://developer.nvidia.com/gpu-deployment-kit. 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. On devices that are capable of concurrent kernel execution, streams can also be used to execute multiple kernels simultaneously to more fully take advantage of the devices multiprocessors. Notwithstanding any damages that customer might incur for any reason whatsoever, NVIDIAs aggregate and cumulative liability towards customer for the products described herein shall be limited in accordance with the Terms of Sale for the product. To obtain best performance in cases where the control flow depends on the thread ID, the controlling condition should be written so as to minimize the number of divergent warps. To illustrate the effect of strided access on effective bandwidth, see the kernel strideCopy() in A kernel to illustrate non-unit stride data copy, which copies data with a stride of stride elements between threads from idata to odata. Customer should obtain the latest relevant information before placing orders and should verify that such information is current and complete. In Overlapping computation and data transfers, the memory copy and kernel execution occur sequentially. To use other CUDA APIs introduced in a minor release (that require a new driver), one would have to implement fallbacks or fail gracefully. For example, Overlapping computation and data transfers demonstrates how host computation in the routine cpuFunction() is performed while data is transferred to the device and a kernel using the device is executed. Can anyone please tell me how to do these two operations? Each warp of threads calculates one row of a tile of C, which depends on a single row of A and an entire tile of B as illustrated in Figure 12. For more information on this pragma, refer to the CUDA C++ Programming Guide. If no new features are used (or if they are used conditionally with fallbacks provided) youll be able to remain compatible. A portion of the L2 cache can be set aside for persistent accesses to a data region in global memory. The context encapsulates kernel launches and memory allocations for that GPU as well as supporting constructs such as the page tables. It enables GPU threads to directly access host memory. The current board power draw and power limits are reported for products that report these measurements. This is evident from the saw tooth curves. Can airtags be tracked from an iMac desktop, with no iPhone? (Note that the CUDA compiler considers any device code that does not contribute to a write to global memory as dead code subject to elimination, so we must at least write something out to global memory as a result of our addressing logic in order to successfully apply this strategy.). 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. Other company and product names may be trademarks of the respective companies with which they are associated. CUDA Toolkit and Minimum Driver Versions. 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). For other algorithms, implementations may be considered correct if they match the reference within some small epsilon. Each component in the toolkit is recommended to be semantically versioned. However, the SONAME of this library is given as libcublas.so.5.5: Because of this, even if -lcublas (with no version number specified) is used when linking the application, the SONAME found at link time implies that libcublas.so.5.5 is the name of the file that the dynamic loader will look for when loading the application and therefore must be the name of the file (or a symlink to the same) that is redistributed with the application. To use CUDA, data values must be transferred from the host to the device. What is a word for the arcane equivalent of a monastery? We cannot declare these directly, but small static allocations go . These many-way bank conflicts are very expensive. They are faster but provide somewhat lower accuracy (e.g., __sinf(x) and __expf(x)). A useful technique to determine the sensitivity of performance to occupancy is through experimentation with the amount of dynamically allocated shared memory, as specified in the third parameter of the execution configuration. For purposes of calculating occupancy, the number of registers used by each thread is one of the key factors. Concurrent kernel execution is described below. An example is transposing [1209, 9] of any type and 32 tile size. Medium Priority: The number of threads per block should be a multiple of 32 threads, because this provides optimal computing efficiency and facilitates coalescing. (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.) This section examines the functionality, advantages, and pitfalls of both approaches. //Set the attributes to a CUDA stream of type cudaStream_t, Mapping Persistent data accesses to set-aside L2 in sliding window experiment, /*Each CUDA thread accesses one element in the persistent data section. The throughput of __sinf(x), __cosf(x), and__expf(x) is much greater than that of sinf(x), cosf(x), and expf(x). libcuda.so on Linux systems). This approach permits some overlapping of the data transfer and execution. Salient Features of Device Memory, Misaligned sequential addresses that fall within five 32-byte segments, Adjacent threads accessing memory with a stride of 2, /* Set aside max possible size of L2 cache for persisting accesses */, // Stream level attributes data structure. For example, the ability to overlap kernel execution with asynchronous data transfers between the host and the device is available on most but not all GPUs irrespective of the compute capability. Register pressure occurs when there are not enough registers available for a given task. The effective bandwidth of this routine is 195.5 GB/s on an NVIDIA Tesla V100. 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. 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. It will now support actual architectures as well to emit SASS. Depending on the value of the num_bytes parameter and the size of L2 cache, one may need to tune the value of hitRatio to avoid thrashing of L2 cache lines. CUDA Compatibility Across Minor Releases, 15.4.1. An application can also use the Occupancy API from the CUDA Runtime, e.g. Recommendations for taking advantage of minor version compatibility in your application, 16.4. Devices to be made visible to the application should be included as a comma-separated list in terms of the system-wide list of enumerable devices. The number of copy engines on a GPU is given by the asyncEngineCount field of the cudaDeviceProp structure, which is also listed in the output of the deviceQuery CUDA Sample. The host code in Zero-copy host code shows how zero copy is typically set up. For recent versions of CUDA hardware, misaligned data accesses are not a big issue. For 32-bit applications, the file would be cublas32_55.dll. An application has no direct control over these bank conflicts. Any PTX device code loaded by an application at runtime is compiled further to binary code by the device driver. Data should be kept on the device as long as possible. Throughout this guide, specific recommendations are made regarding the design and implementation of CUDA C++ code. The following table presents the evolution of matrix instruction sizes and supported data types for Tensor Cores across different GPU architecture generations. Tuning the Access Window Hit-Ratio, 9.2.3.2. NVIDIA hereby expressly objects to applying any customer general terms and conditions with regards to the purchase of the NVIDIA product referenced in this document. For example, transferring two matrices to the device to perform a matrix addition and then transferring the results back to the host will not realize much performance benefit. The versions of the components in the toolkit are available in this table. Let's say that there are m blocks. Therefore, the compiler can optimize more aggressively with signed arithmetic than it can with unsigned arithmetic. Furthermore, the pinning of system memory is a heavyweight operation compared to most normal system memory allocations, so as with all optimizations, test the application and the systems it runs on for optimal performance parameters. How to time code using CUDA events illustrates their use. As described in Memory Optimizations of this guide, bandwidth can be dramatically affected by the choice of memory in which data is stored, how the data is laid out and the order in which it is accessed, as well as other factors. Customer should obtain the latest relevant information before placing orders and should verify that such information is current and complete. Recommendations for building a minor-version compatible library, 15.4.1.5. When an application will be deployed to target machines of arbitrary/unknown configuration, the application should explicitly test for the existence of a CUDA-capable GPU in order to take appropriate action when no such device is available. A shared memory request for a warp is not split as with devices of compute capability 1.x, meaning that bank conflicts can occur between threads in the first half of a warp and threads in the second half of the same warp. Furthermore, there should be multiple active blocks per multiprocessor so that blocks that arent waiting for a __syncthreads() can keep the hardware busy. Block-column matrix (A) multiplied by block-row matrix (B) with resulting product matrix (C). The context is explicit in the CUDA Driver API but is entirely implicit in the CUDA Runtime API, which creates and manages contexts automatically. For devices of compute capability 1.x, the warp size is 32 threads and the number of banks is 16. For example, on devices of compute capability 7.0 each multiprocessor has 65,536 32-bit registers and can have a maximum of 2048 simultaneous threads resident (64 warps x 32 threads per warp). CUDA applications are built against the CUDA Runtime library, which handles device, memory, and kernel management. Likewise, for exponentation with an exponent of -1/3, use rcbrt() or rcbrtf(). GPUs with a single copy engine can perform one asynchronous data transfer and execute kernels whereas GPUs with two copy engines can simultaneously perform one asynchronous data transfer from the host to the device, one asynchronous data transfer from the device to the host, and execute kernels. For example, on IBM Newell POWER9 nodes (where the CPUs correspond to NUMA nodes 0 and 8), use: One of the keys to good performance is to keep the multiprocessors on the device as busy as possible. What if you need multiple dynamically sized arrays in a single kernel? They can be distinguished by their names: some have names with prepended underscores, whereas others do not (e.g., __functionName() versus functionName()). By simply increasing this parameter (without modifying the kernel), it is possible to effectively reduce the occupancy of the kernel and measure its effect on performance. 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. I have locally sorted queues in different blocks of cuda. To understand the performance difference between synchronous copy and asynchronous copy of data from global memory to shared memory, consider the following micro benchmark CUDA kernels for demonstrating the synchronous and asynchronous approaches. Therefore, to get the largest speedup for a fixed problem size, it is worthwhile to spend effort on increasing P, maximizing the amount of code that can be parallelized. 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. The NVIDIA A100 GPU increases the HBM2 memory capacity from 32 GB in V100 GPU to 40 GB in A100 GPU. Instead, strategies can be applied incrementally as they are learned. These examples assume compute capability 6.0 or higher and that accesses are for 4-byte words, unless otherwise noted. In such cases, users or developers can still benefit from not having to upgrade the entire CUDA Toolkit or driver to use these libraries or frameworks. Not using intermediate registers can help reduce register pressure and can increase kernel occupancy. When using a shared or static library, follow the release notes of said library to determine if the library supports minor version compatibility. In both cases, kernels must be compiled into binary code by nvcc (called cubins) to execute on the device. As with the dynamically-linked version of the CUDA Runtime library, these libraries should be bundled with the application executable when distributing that application. 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 criteria of benefit and scope for establishing priority will vary depending on the nature of the program. This action leads to a load of eight L2 cache segments per warp on the Tesla V100 (compute capability 7.0). The following example is based on gprof, which is an open-source profiler for Linux platforms from the GNU Binutils collection. This is because the user could only allocate the CUDA static shared memory up to 48 KB. 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. Because it is on-chip, shared memory has much higher bandwidth and lower latency than local and global memory - provided there are no bank conflicts between the threads, as detailed in the following section. The dynamic shared memory kernel, dynamicReverse(), declares the shared memory array using an unsized extern array syntax, extern shared int s[] (note the empty brackets and use of the extern specifier). The maximum number of registers per thread is 255. Reinitialize the GPU hardware and software state via a secondary bus reset. BFloat16 format is especially effective for DL training scenarios. For example, to use only devices 0 and 2 from the system-wide list of devices, set CUDA_VISIBLE_DEVICES=0,2 before launching the application. The OpenACC standard provides a set of compiler directives to specify loops and regions of code in standard C, C++ and Fortran that should be offloaded from a host CPU to an attached accelerator such as a CUDA GPU. 1 Answer Sorted by: 2 You don't need to worry about this. The compiler will perform these conversions if n is literal. The NVIDIA Management Library (NVML) is a C-based interface that provides direct access to the queries and commands exposed via nvidia-smi intended as a platform for building 3rd-party system management applications. Randomly accessing. These recommendations are categorized by priority, which is a blend of the effect of the recommendation and its scope. When attempting to optimize CUDA code, it pays to know how to measure performance accurately and to understand the role that bandwidth plays in performance measurement. Regardless of this possibility, it is good practice to verify that no higher-priority recommendations have been overlooked before undertaking lower-priority items. The following throughput metrics can be displayed in the Details or Detail Graphs view: The Requested Global Load Throughput and Requested Global Store Throughput values indicate the global memory throughput requested by the kernel and therefore correspond to the effective bandwidth obtained by the calculation shown under Effective Bandwidth Calculation. Whether a device has this capability is indicated by the concurrentKernels field of the cudaDeviceProp structure (or listed in the output of the deviceQuery CUDA Sample). Because the data is not cached on the GPU, mapped pinned memory should be read or written only once, and the global loads and stores that read and write the memory should be coalesced. On devices of compute capability 5.x or newer, each bank has a bandwidth of 32 bits every clock cycle, and successive 32-bit words are assigned to successive banks. In the previous post, I looked at how global memory accesses by a group of threads can be coalesced into a single transaction, and howalignment and stride affect coalescing for various generations of CUDA hardware. However, bank conflicts occur when copying the tile from global memory into shared memory. If an appropriate native binary (cubin) is not available, but the intermediate PTX code (which targets an abstract virtual instruction set and is used for forward-compatibility) is available, then the kernel will be compiled Just In Time (JIT) (see Compiler JIT Cache Management Tools) from the PTX to the native cubin for the device. Shared Memory in Matrix Multiplication (C=AB), 9.2.3.3. Although each of these instructions is scheduled for execution, only the instructions with a true predicate are actually executed. Before tackling other hotspots to improve the total speedup, the developer should consider taking the partially parallelized implementation and carry it through to production. Concurrent copy and execute demonstrates how to overlap kernel execution with asynchronous data transfer. Registers are allocated to an entire block all at once. See Registers for details. When using CPU timers, it is critical to remember that many CUDA API functions are asynchronous; that is, they return control back to the calling CPU thread prior to completing their work. High Priority: Ensure global memory accesses are coalesced whenever possible. NVLink operates transparently within the existing CUDA model. "After the incident", I started to be more careful not to trip over things. Shared memory is a CUDA memory space that is shared by all threads in a thread block. Therefore, in terms of wxw tiles, A is a column matrix, B is a row matrix, and C is their outer product; see Figure 11. CUDA 11.0 introduces an async-copy feature that can be used within device code to explicitly manage the asynchronous copying of data from global memory to shared memory. This new feature is exposed via the pipeline API in CUDA. So, if each thread block uses many registers, the number of thread blocks that can be resident on a multiprocessor is reduced, thereby lowering the occupancy of the multiprocessor. In order to maintain forward compatibility to future hardware and toolkits and to ensure that at least one thread block can run on an SM, developers should include the single argument __launch_bounds__(maxThreadsPerBlock) which specifies the largest block size that the kernel will be launched with.