Both the CUDA driver and the CUDA runtime are not source compatible across the different SDK releases. NVIDIA reserves the right to make corrections, modifications, enhancements, improvements, and any other changes to this document, at any time without notice. Shared memory can be thought of as a software-controlled cache on the processor - each Streaming Multiprocessor has a small amount of shared memory (e.g. compute_80). 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. Here cudaEventRecord() is used to place the start and stop events into the default stream, stream 0. The compiler will perform these conversions if n is literal. To assist with this, the CUDA Driver API provides methods to access and manage a special context on each GPU called the primary context. However, bank conflicts occur when copying the tile from global memory into shared memory. When using NVRTC, it is recommended that the resulting PTX code is first transformed to the final device code via the steps outlined by the PTX user workflow. The current GPU core temperature is reported, along with fan speeds for products with active cooling. After this change, the effective bandwidth is 199.4 GB/s on an NVIDIA Tesla V100, which is comparable to the results from the last C = AB kernel. (The performance advantage sinpi() has over sin() is due to simplified argument reduction; the accuracy advantage is because sinpi() multiplies by only implicitly, effectively using an infinitely precise mathematical rather than a single- or double-precision approximation thereof.). Clear single-bit and double-bit ECC error counts. This kernel has an effective bandwidth of 144.4 GB/s on an NVIDIA Tesla V100. Unlike the CUDA Driver, the CUDA Runtime guarantees neither forward nor backward binary compatibility across versions. With wrap, x is replaced by frac(x) where frac(x) = x - floor(x). Increment major versions when there are ABI breaking changes such as API deprecation and modifications. \times (4096/8) \times 2 \right) \div 10^{9} = 898\text{GB/s}\). 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. 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. When using the driver APIs directly, we recommend using the new driver entry point access API (cuGetProcAddress) documented here: CUDA Driver API :: CUDA Toolkit Documentation. How to manage this resource utilization is discussed in the final sections of this chapter. Hardware utilization can also be improved in some cases by designing your application so that multiple, independent kernels can execute at the same time. Finally, particular attention must be paid to control flow instructions due to the SIMT (single instruction multiple thread) nature of the device. The maximum number of thread blocks per SM is 32 for devices of compute capability 8.0 (i.e., A100 GPUs) and 16 for GPUs with compute capability 8.6. With UVA, the host memory and the device memories of all installed supported devices share a single virtual address space. However, a few rules of thumb should be followed: Threads per block should be a multiple of warp size to avoid wasting computation on under-populated warps and to facilitate coalescing. This chapter discusses the various kinds of memory on the host and device and how best to set up data items to use the memory effectively. Block-column matrix (A) multiplied by block-row matrix (B) with resulting product matrix (C). Devices of compute capability 2.0 and later support a special addressing mode called Unified Virtual Addressing (UVA) on 64-bit Linux and Windows. This ensures your code is compatible. 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. The last argument to the cudaMemcpyAsync() function is the stream ID, which in this case uses the default stream, stream 0. There are a number of tools that can be used to generate the profile. Along with the increased memory capacity, the bandwidth is increased by 72%, from 900 GB/s on Volta V100 to 1550 GB/s on A100. For other algorithms, implementations may be considered correct if they match the reference within some small epsilon. The actual memory throughput shows how close the code is to the hardware limit, and a comparison of the effective or requested bandwidth to the actual bandwidth presents a good estimate of how much bandwidth is wasted by suboptimal coalescing of memory accesses (see Coalesced Access to Global Memory). Of these different memory spaces, global memory is the most plentiful; see Features and Technical Specifications of the CUDA C++ Programming Guide for the amounts of memory available in each memory space at each compute capability level. This makes the code run faster at the cost of diminished precision and accuracy. In particular, there is no register-related reason to pack data into vector data types such as float4 or int4 types. The primary differences are in threading model and in separate physical memories: Execution pipelines on host systems can support a limited number of concurrent threads. In particular, developers should note the number of multiprocessors on the device, the number of registers and the amount of memory available, and any special capabilities of the device. Prior to CUDA 11.0, the minimum driver version for a toolkit was the same as the driver shipped with that version of the CUDA Toolkit. Information published by NVIDIA regarding third-party products or services does not constitute a license from NVIDIA to use such products or services or a warranty or endorsement thereof. This padding eliminates the conflicts entirely, because now the stride between threads is w+1 banks (i.e., 33 for current devices), which, due to modulo arithmetic used to compute bank indices, is equivalent to a unit stride. The NVIDIA A100 GPU supports shared memory capacity of 0, 8, 16, 32, 64, 100, 132 or 164 KB per SM. To check for errors occurring during kernel launches using the <<<>>> syntax, which does not return any error code, the return code of cudaGetLastError() should be checked immediately after the kernel launch. The NVIDIA Ampere GPU architecture adds hardware acceleration for copying data from global memory to shared memory. However, it also can act as a constraint on occupancy. (e.g. Scattered accesses increase ECC memory transfer overhead, especially when writing data to global memory. The CUDA Toolkit libraries (cuBLAS, cuFFT, etc.) It will now support actual architectures as well to emit SASS. The most important consideration with any profiling activity is to ensure that the workload is realistic - i.e., that information gained from the test and decisions based upon that information are relevant to real data. 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. Certain memory access patterns enable the hardware to coalesce groups of reads or writes of multiple data items into one operation. cudaOccupancyMaxActiveBlocksPerMultiprocessor, to dynamically select launch configurations based on runtime parameters. Other differences are discussed as they arise elsewhere in this document. Applications compiled against a CUDA Toolkit version will only run on systems with the specified minimum driver version for that toolkit version. 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. As a result, a read from constant memory costs one memory read from device memory only on a cache miss; otherwise, it just costs one read from the constant cache. The following table presents the evolution of matrix instruction sizes and supported data types for Tensor Cores across different GPU architecture generations. First, we set aside 30 MB of the L2 cache for persisting accesses using cudaDeviceSetLimit(), as discussed above. Like the other calls in this listing, their specific operation, parameters, and return values are described in the CUDA Toolkit Reference Manual. There is no way to check this for a specific variable, but the compiler reports total local memory usage per kernel (lmem) when run with the--ptxas-options=-v option. if several threads had accessed the same word or if some threads did not participate in the access), the full segment is fetched anyway. Device 0 of this system has compute capability 7.0. In this calculation, the memory clock rate is converted in to Hz, multiplied by the interface width (divided by 8, to convert bits to bytes) and multiplied by 2 due to the double data rate. Starting with the Volta architecture, Independent Thread Scheduling allows a warp to remain diverged outside of the data-dependent conditional block. Adjust kernel launch configuration to maximize device utilization. UVA is also a necessary precondition for enabling peer-to-peer (P2P) transfer of data directly across the PCIe bus or NVLink for supported GPUs in supported configurations, bypassing host memory. This is possible because the distribution of the warps across the block is deterministic as mentioned in SIMT Architecture of the CUDA C++ Programming Guide. When our CUDA 11.1 application (i.e. Performance Improvements Optimizing C = AB Matrix Multiply Computing a row of a tile in C using one row of A and an entire tile of B. Devices of compute capability 2.0 and higher have the additional ability to multicast shared memory accesses, meaning that multiple accesses to the same location by any number of threads within a warp are served simultaneously. Memory instructions include any instruction that reads from or writes to shared, local, or global memory. In order to profit from any modern processor architecture, GPUs included, the first steps are to assess the application to identify the hotspots, determine whether they can be parallelized, and understand the relevant workloads both now and in the future. By comparison, the smallest executable unit of parallelism on a CUDA device comprises 32 threads (termed a warp of threads). Low Priority: Avoid automatic conversion of doubles to floats. The reciprocal square root should always be invoked explicitly as rsqrtf() for single precision and rsqrt() for double precision. Overall, best performance is achieved when using asynchronous copies with an element of size 8 or 16 bytes. How do you ensure that a red herring doesn't violate Chekhov's gun? shared memory needed per block is lenP + lenS (where lenS is your blocksize + patternlength) the kernel assumes that gridDim.x * blockDim.x = lenT (the same as version 1) we can copy into shared memory in parallel (no need for for loops if you have enough threads) Share Improve this answer Follow edited Apr 15, 2011 at 19:59 This should be our first candidate function for parallelization. NVIDIA products are not designed, authorized, or warranted to be suitable for use in medical, military, aircraft, space, or life support equipment, nor in applications where failure or malfunction of the NVIDIA product can reasonably be expected to result in personal injury, death, or property or environmental damage. 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. Compatibility of the CUDA platform is thus intended to address a few scenarios: NVIDIA driver upgrades to systems with GPUs running in production for enterprises or datacenters can be complex and may need advance planning. The effective bandwidth of this kernel is 140.2 GB/s on an NVIDIA Tesla V100.These results are lower than those obtained by the final kernel for C = AB. Therefore, it is important to be sure to compare values of like precision and to express the results within a certain tolerance rather than expecting them to be exact. Then, as shown in the figure below, we specify that the accesses to the first freqSize * sizeof(int) bytes of the memory region are persistent. Conversion to Warp isn't possible for Week 6-7 because there is no support for shared memory or block level synchronization. For portability, that is, to be able to execute code on future GPU architectures with higher compute capability (for which no binary code can be generated yet), an application must load PTX code that will be just-in-time compiled by the NVIDIA driver for these future devices. Not all threads need to participate. For more information please refer to the section on Async Copy in the CUDA C++ Programming Guide. As an exception, scattered writes to HBM2 see some overhead from ECC but much less than the overhead with similar access patterns on ECC-protected GDDR5 memory. Devices of compute capability 3.x have configurable bank size, which can be set using cudaDeviceSetSharedMemConfig() to either four bytes (cudaSharedMemBankSizeFourByte, thedefault) or eight bytes (cudaSharedMemBankSizeEightByte). 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. It is limited. Local memory is so named because its scope is local to the thread, not because of its physical location. Best practices suggest that this optimization be performed after all higher-level optimizations have been completed. Declare shared memory in CUDA C/C++ device code using the__shared__variable declaration specifier. However, striding through global memory is problematic regardless of the generation of the CUDA hardware, and would seem to be unavoidable in many cases, such as when accessing elements in a multidimensional array along the second and higher dimensions. The difference between the phonemes /p/ and /b/ in Japanese. Also of note is the Thrust library, which is a parallel C++ template library similar to the C++ Standard Template Library. Customer should obtain the latest relevant information before placing orders and should verify that such information is current and complete. Using a profiler, the developer can identify such hotspots and start to compile a list of candidates for parallelization. The interface is augmented to retrieve either the PTX or cubin if an actual architecture is specified. Therefore, an application that compiled successfully on an older version of the toolkit may require changes in order to compile against a newer version of the toolkit. sm_80) rather than a virtual architecture (e.g. For GPUs with compute capability 8.6 maximum shared memory per thread block is 99 KB. However, based on what you've described here, your algorithm might be amenable to an approach similar to what is outlined in the threadfence reduction sample. Even a relatively slow kernel may be advantageous if it avoids one or more transfers between host and device memory. The new Tensor Cores use a larger base matrix size and add powerful new math modes including: Support for FP64 Tensor Core, using new DMMA instructions. When sharing data between threads, we need to be careful to avoid race conditions, because while threads in a block run logically in parallel, not all threads can execute physically at the same time. However, it is best to avoid accessing global memory whenever possible. CUDA Refresher: The CUDA Programming Model - NVIDIA Technical Blog At a minimum, you would need some sort of selection process that can access the heads of each queue. Integer division and modulo operations are particularly costly and should be avoided or replaced with bitwise operations whenever possible: If \(n\) is a power of 2, ( \(i/n\) ) is equivalent to ( \(i \gg {log2}(n)\) ) and ( \(i\% n\) ) is equivalent to ( \(i\&\left( {n - 1} \right)\) ). Package managers facilitate this process but unexpected issues can still arise and if a bug is found, it necessitates a repeat of the above upgrade process. If we validate our addressing logic separately prior to introducing the bulk of the computation, then this will simplify any later debugging efforts. This means that even though an application source might need to be changed if it has to be recompiled against a newer CUDA Toolkit in order to use the newer features, replacing the driver components installed in a system with a newer version will always support existing applications and its functions. This capability makes them well suited to computations that can leverage parallel execution. There are many such factors involved in selecting block size, and inevitably some experimentation is required. Last updated on Feb 27, 2023. cudaFuncAttributePreferredSharedMemoryCarveout, 1. Both correctable single-bit and detectable double-bit errors are reported. For applications that need additional functionality or performance beyond what existing parallel libraries or parallelizing compilers can provide, parallel programming languages such as CUDA C++ that integrate seamlessly with existing sequential code are essential. All rights reserved. For exponentiation using base 2 or 10, use the functions exp2() or expf2() and exp10() or expf10() rather than the functions pow() or powf(). 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. Effective bandwidth is calculated by timing specific program activities and by knowing how data is accessed by the program. This means that in one of these devices, for a multiprocessor to have 100% occupancy, each thread can use at most 32 registers. The high-priority recommendations from those guides are as follows: Find ways to parallelize sequential code. Sometimes, the compiler may unroll loops or optimize out if or switch statements by using branch predication instead. We cannot declare these directly, but small static allocations go . In particular, a larger block size does not imply a higher occupancy. The CUDA driver ensures backward Binary Compatibility is maintained for compiled CUDA applications. This guide introduces the Assess, Parallelize, Optimize, Deploy(APOD) design cycle for applications with the goal of helping application developers to rapidly identify the portions of their code that would most readily benefit from GPU acceleration, rapidly realize that benefit, and begin leveraging the resulting speedups in production as early as possible. In contrast with cudaMemcpy(), the asynchronous transfer version requires pinned host memory (see Pinned Memory), and it contains an additional argument, a stream ID. Shared memory is extremely fast, user managed, on-chip memory that can be used to share data between threads within a thread block. cudaDeviceSynchronize()blocks the calling CPU thread until all CUDA calls previously issued by the thread are completed. We can see this usage in the following example: NVRTC is a runtime compilation library for CUDA C++. Global, local, and texture memory have the greatest access latency, followed by constant memory, shared memory, and the register file. // Type of access property on cache miss. On integrated GPUs (i.e., GPUs with the integrated field of the CUDA device properties structure set to 1), mapped pinned memory is always a performance gain because it avoids superfluous copies as integrated GPU and CPU memory are physically the same. The CUDA Runtime API provides developers with high-level C++ interface for simplified management of devices, kernel executions etc., While the CUDA driver API provides (CUDA Driver API) a low-level programming interface for applications to target NVIDIA hardware. Recall that the initial assess step allowed the developer to determine an upper bound for the potential speedup attainable by accelerating given hotspots. Shared Memory and Synchronization - GPU Programming In our use case, BLOCK_SIZE + 2 * RADIUS = $1024 + 2 \times 6000$ = $13024$ and the size of an int is $4$ Byte, therefore, the shared memory required is $17024 \times 4 / 1024$ = $50.875$ KB, which is larger than the maximum static shared memory we could have. For an existing project, the first step is to assess the application to locate the parts of the code that are responsible for the bulk of the execution time. Several third-party debuggers support CUDA debugging as well; see: https://developer.nvidia.com/debugging-solutions for more details. 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). By comparison, threads on GPUs are extremely lightweight. Now that we are working block by block, we should use shared memory. Applications using the new API can load the final device code directly using driver APIs cuModuleLoadData and cuModuleLoadDataEx. Cornell Virtual Workshop: Memory Architecture To learn more, see our tips on writing great answers. If the transfer time exceeds the execution time, a rough estimate for the overall time is tT + tE/nStreams. What is CUDA memory? - Quora This capability (combined with thread synchronization) has a number of uses, such as user-managed data caches, high-performance cooperative parallel algorithms (parallel reductions, for example), and tofacilitate global memory coalescing in cases where it would otherwise not be possible. Access to shared memory is much faster than global memory access because it is located on a chip. 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 key concept in this effort is occupancy, which is explained in the following sections. Conversely, if P is a small number (meaning that the application is not substantially parallelizable), increasing the number of processors N does little to improve performance. What if you need multiple dynamically sized arrays in a single kernel? If all threads of a warp access the same location, then constant memory can be as fast as a register access. The context is explicit in the CUDA Driver API but is entirely implicit in the CUDA Runtime API, which creates and manages contexts automatically. If this happens, the different execution paths must be executed separately; this increases the total number of instructions executed for this warp. Table 2. As with the dynamically-linked version of the CUDA Runtime library, these libraries should be bundled with the application executable when distributing that application. Devices of compute capability 1.3 and higher provide native support for double-precision floating-point values (that is, values 64 bits wide). outside your established ABI contract. 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. Having understood the application profile, the developer should understand how the problem size would change if the computational performance changes and then apply either Amdahls or Gustafsons Law to determine an upper bound for the speedup. The value of this field is propagated into an application built against the library and is used to locate the library of the correct version at runtime. In this code, the canMapHostMemory field of the structure returned by cudaGetDeviceProperties() is used to check that the device supports mapping host memory to the devices address space. When you parallelize computations, you potentially change the order of operations and therefore the parallel results might not match sequential results. Before implementing lower priority recommendations, it is good practice to make sure all higher priority recommendations that are relevant have already been applied. 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. Flow control instructions (if, switch, do, for, while) can significantly affect the instruction throughput by causing threads of the same warp to diverge; that is, to follow different execution paths. High Priority: Avoid different execution paths within the same warp. Using the CUDA Occupancy Calculator to project GPU multiprocessor occupancy. In short, CPU cores are designed to minimize latency for a small number of threads at a time each, whereas GPUs are designed to handle a large number of concurrent, lightweight threads in order to maximize throughput. Consider the following kernel code and access window parameters, as the implementation of the sliding window experiment. Low Priority: Use shift operations to avoid expensive division and modulo calculations. 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. For Windows 8, SetDefaultDLLDirectories() and AddDllDirectory() should be used instead of SetDllDirectory(). For more information on the Arrive/Wait Barriers refer to the Arrive/Wait Barrier section in the CUDA C++ Programming Guide. For codes continuing to make use of PTX, in order to support compiling on an older driver, your code must be first transformed into device code via the static ptxjitcompiler library or NVRTC with the option of generating code for a specific architecture (e.g. The --ptxas options=v option of nvcc details the number of registers used per thread for each kernel.