In the output from this build attempt, we see that cuda_memcmp is identified as an invalid constexpr function because it does not return a constant expression. Generally reinterpret_cast is much less restrictive than other C++ style casts in that it will allow you to cast most types to most other types which is both it's strength and weakness. Along with this flexibility comes decisions for tuning and usage. 0x1. Is it appropriate to ignore emails from a student asking obvious questions? Describe the bug I just want to make sure about that since some of the functions I wrote depend on it. So if you have a kernel that is already register limited or has very low parallelism, you may want to stick to scalar loads. reinterpret_cast is a tricky beast. When does casting change a value's bits in C++? Lets begin by looking at the following simple memory copy kernel. Therefore whatever alignment conditions exist will not be affected by that kind of cast. Therefore whatever alignment conditions exist will not be affected by that kind of cast. reinterpret_cast reinterpret_cast static_cast TensorRT/blob/master/samples/common/common.h readPGMFile inline void readPGMFile(const std::string& fileName, uint8_t* buffer, int inH, int inW) { //. It may help you to figure out the problem yourself, and even if not, the error output will be useful for others trying to help you. I discovered this while trying to set up IWYU for cuml (and hopefully other RAPIDS projects eventually). Will there be any performance difference(optimizations) with using reinterpret_cast within the kernel vs. casting in the kernel call from host? This requires compilation with clang. This makes it very important to take steps to mitigate bandwidth bottlenecks in your code. It's used primarily for things like turning a raw data bit stream into actual data or storing data in the low bits of an aligned pointer. Another issue is that some of the aligned types will be loaded via __ldg(). For each model running with each execution provider, there are settings that can be tuned (e . The short answer: If you don't know what reinterpret_cast stands for, don't use it. In this post, I will show you how to use vector loads and stores in CUDA C/C++ to help increase bandwidth utilization while decreasing the number of executed instructions. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. advantage to using reinterpret_cast . You can easily use these types via type casting in C/C++. Because of the function, cudaMalloc will automatcially fullfill some aligment requirements (I think it is aligned to some 128 byte memory boundary), therefore I think both SomeDevIntPtr and SomeDevPtr should be start at exact the same physical memory address at GPU's global memory, am I correct on this? Convolution-heavy models and the CUDA EP ORT leverages CuDNN for convolution operations and the first step in this process is to determine which "optimal" convolution algorithm to use while performing the convolution operation for the given input configuration (input shape, filter shape, etc.) The rule of the thumb should be: Never use reinterpret_cast or C-Style casting, if you need to cast pointers, cast them via void*, and only if absolutely necessary use reinterpret_cast - that means, if you really have to reinterpret the data. reinterpret\u cast "" @255 You signed in with another tab or window. First, the loop now executes only N/2 times because each iteration processes two elements. Forcing a conversion that could happen anyway: double d = 4.5; To learn more, see our tips on writing great answers. About Pointer alignment, is there a way to make a pointer aligned to some given memory boundary? V Jyothi. GCC allows this code, but I would expect/hope that compilation would succeed with the (in this case) more conformant clang compilation rules. I usually recommend that for everyone, before asking for help. A reinterpret_cast of a pointer to a pointer does not (ie. Connect and share knowledge within a single location that is structured and easy to search. . C++reinterpret_cast. Perhaps it also depends on where that recast ends up - local, shared or global memory, I suppose you only need to recast the array once; otherwise permanently recasting the array becomes feasible, Powered by Discourse, best viewed with JavaScript enabled. It is important to remember that even though a program compiles, its . If an operator called multiple kernels during execution, the performance numbers of those kernels will all be listed following the call sequence: It is used to convert a pointer of some data type into a pointer of another data type, even if the data types before and after conversion are different. [] Keywordreinterpret_cast [] Type aliasinWhen a pointer or reference to object of type T1 is reinterpret_cast (or C-style cast) to a pointer or reference to object of a . Contents Install Requirements Build Configuration Options Samples Performance Tuning Install Pre-built binaries of ONNX Runtime with CUDA EP are published for most language bindings. We do not currently allow content pasted from ChatGPT on Stack Overflow; read our policy here. This 2x improvement in instruction count is very important in instruction-bound or latency-bound kernels. reinterpret_cast < new-type > ( expression ) Returns a value of type new-type . Not sure if it was just me or something she sent to the whole team. Vectorized loads are a fundamental CUDA optimization that you should use when possible, because they increase bandwidth, reduce instruction count, and reduce latency. . should not) change the underlying numerical value (bit pattern representation) of a pointer. Some CUDA pointers need to be naturally aligned. veca = reinterpret_cast<int4*>(&a[1])[0]; Suggestion: run your code with cuda-memcheck. The C++ compiler detects and quietly fixes most but not all violations. Syntax static_cast < new-type > ( expression ) Returns a value of type new-type . Expected behavior ONNX Runtime provides high performance across a range of hardware options through its Execution Providers interface for different execution environments. Why does the distance from light to subject affect exposure (inverse square law) while from subject to lens does not? For each model running with each execution provider, there are settings that can be tuned (e . Why would Henry want to close the breach? You can safely offset arrays if you use an aligned offset, as inreinterpret_cast(d_in+2). CUDA Dynamic Parallelism API and Principles, CUDA Pro Tip: Write Flexible Kernels with Grid-Stride Loops, An Efficient Matrix Transpose in CUDA C/C++, How to Optimize Data Transfers in CUDA C/C++, How to Optimize Data Transfers in CUDA Fortran, AI Models Recap: Scalable Pretrained Models Across Industries, X-ray Research Reveals Hazards in Airport Luggage Using Crystal Physics, Sharpen Your Edge AI and Robotics Skills with the NVIDIA Jetson Nano Developer Kit, Designing an Optimal AI Inference Pipeline for Autonomous Driving, NVIDIA Grace Hopper Superchip Architecture In-Depth. So, in our case, the inputs are an integer giving the dimension of the problem . Explanation Unlike static_cast, but like const_cast, the reinterpret_cast expression does not compile to any CPU instructions (except when converting between integers and pointers or on obscure architectures where pointer representation depends on its type). In cuda_memcmp, which is declared constexpr, two reinterpret_cast calls appear, as shown here. Penrose diagram of hypothetical astrophysical white hole. reinterpret_cast is usually used for casting unrelated types. NPP will evolve over time to encompass more of the compute heavy tasks in a variety of problem domains. privacy statement. reinterpret_cast. What is the difference between (void **)&x and (void *)x? GELU dtype = float32shapeNVIDIA A100-PCIE-40GB. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. This version of the code has reduced the instruction count by a factor of 4. reinterpret_cast <cuDoubleComplex*>(c), ldc, stridec, num_batches));} template <> . To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Making statements based on opinion; back them up with references or personal experience. You can also generate vectorized loads using structures as long as the structure is a power of two bytes in size. . We can improve performance of this operation by using the vectorized load and store instructions LD.E. The result of a reinterpret_cast cannot safely be used for anything other than being cast back to its original type. Using vectorized loads reduces the total number of instructions, reduces latency, and improves bandwidth utilization. In other words will it be fundamentally the same if I do this; (within kernel with arr as float(assuming correct indexing is done) ). Asking for help, clarification, or responding to other answers. At what point in the prequels is it revealed that Palpatine is Darth Sidious? Remove constexpr from bitwise_compare functions. How did muzzle-loaded rifled artillery solve the problems of the hand-held rifle? Why does the USA not have a constitutional court? Ready to optimize your JavaScript with Rust? We can also write a vector4 version of the copy kernel. Many CUDA kernels are bandwidth bound, and the increasing ratio of flops to bandwidth in new hardware results in more bandwidth bound kernels. For example reinterpret_cast(d_in+1) is invalid because d_in+1 is not aligned to a multiple of sizeof(int2). At times, I think that both approaches predominantly constitute the same and same amount of work, such that there can not be a significant performance difference - my thinking is that in both cases, much of the recasting would be done by threads Implementing realloc in CUDA without moving data. Are there breakers which can be triggered by an external signal and have to be reset by hand? ONNX Runtime provides high performance across a range of hardware options through its Execution Providers interface for different execution environments. Why is the eastern United States green if the wind moves from west to east? It's sole purpose is to indicate to the compiler that you want to take some bits and pretend that they represent this other type. Example: Environment details (please complete the following information): Additional context rev2022.12.9.43105. Inspecting the SASS we see the following. Tuned OpenCL BLAS. The four IMAD instructions compute the load and store addresses and the LD.E and ST.E load and store 32 bits from those addresses. {64,128} and ST.E.{64,128}. Here we can see the generated LD.E.128 and ST.E.128. How can I use a VPN to access a Russian website that is banned in the EU? My way of describing static_cast is that it supports two functions: 1. The easiest way to use vectorized loads is to use the vector data types defined in the CUDA C/C++ standard headers, such as int2, int4, orfloat2. The easiest way to use vectorized loads is to use the vector data types defined in the CUDA C/C++ standard headers, such as int2, int4, or float2. // On CUDA versions prior to 11, users are required to set the math mode to CUBLAS_TENSOR_OP_MATH // manually to be able to use tensor cores for FP16. In C99 you can do the same thing using the casting operator: (int2*(d_in)). Now that we have seen how to generate vectorized instructions lets modify the memory copy kernel to use vector loads. I will load onnx model using ONNX Runtime C++ API. The reinterpret_cast operator, as well as the other named cast operators, is more easily spotted than C-style casts, and highlights the paradox of a strongly typed language that allows explicit casts. Requirements to your account. Device-allocated memory is automatically aligned to a multiple of the size of the data type, but if you offset the pointer the offset must also be aligned. Updated on 23-Jun-2020 13:57:11. It does not check if the pointer type and data pointed by the pointer is same or not. Should I give a brutally honest feedback on course evaluations? Sign in As described in the XLA documentation, the signature for a CPU XLA custom call in C++ is: void custom_call ( void * out, const void ** in); where, as you might expect, the elements of in point to the input values. Also, as discussed earlier, if your pointer is not aligned or your data type size in bytes is not a power of two you cannot use vectorized loads. The C++ standard does not allow this (see item 17 here), and when clang is used as the host compiler, it (correctly, I believe) throws an error. . By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. This kernel has only a few changes. Please reference Install ORT. The text was updated successfully, but these errors were encountered: Successfully merging a pull request may close this issue. You can debug python convert_to_onnx.py -m gpt2 -o to see how the function is used in inference.. Thanks for contributing an answer to Stack Overflow! In almost all cases vectorized loads are preferable to scalar loads. The primary set of functionality in the library focuses on image processing and is widely applicable for developers in these areas. Not the answer you're looking for? It's possible of course, to cast a properly aligned pointer to a type that no longer has proper alignment. Along with this flexibility comes decisions for tuning and usage. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. auto * cuda_stream = stream-> As <CudaStream> (); OF_CUDA_CHECK ( (cuda::elementwise::Unary<CastFunctor<To, From>, To, From> ( CastFunctor<To, From> (), count, reinterpret_cast <To*> (to), reinterpret_cast < const From*> (from), cuda_stream-> cuda_stream ()))); } }; template < typename From, typename To> std::unique_ptr<Cast> NewCast () { You can see the overall performance for all 3 kernels in Figure 2. How to smoothen the round border of a created buffer to make it look more natural? Fixed by #96 Contributor wphicks commented on Jul 16, 2021 Environment location: Bare-metal Method of PROJECT install: from source wphicks added the type: bug label on Jul 16, 2021 Notice that now the compiler generates LD.E.64 and ST.E.64. Can you give me some advice? For example in C++ you can recast the int pointer d_in to an int2 pointer using reinterpret_cast(d_in). How big is the array, and how many times do you recast (the array)? The SASS for the body of the scalar copy kernel is the following: Here we can see a total of six instructions associated with the copy operation. I have a pointer to a device array of floats (starts off as float type), but intend to read in kernel as a float4 type. in each Conv node. Hebrews 1:3 What is the Relationship Between Jesus and The Word of His Power? This is the function that we want to expose to JAX. However, it is important to note that there will be half as many instructions executed because the loop only executes N/2 times. ONNX Runtime Performance Tuning. These operations also load and store data but do so in 64- or 128-bit widths. However, there is one important caveat: these instructions requirealigned data. By clicking Sign up for GitHub, you agree to our terms of service and You can easily use these types via type casting in C/C++. (calling from host) kernel<<<>> ( (float4*) (&arr [0]),) Or this; (within kernel with arr as float (assuming correct indexing is done) ) = reinterpret_cast<float4*> (arr) [offset]; EDIT: so far in my testing of both methods, there seems to not be much of a difference. In this code, I am using grid-stride loops, described in anearlier CUDA Pro Tip post. So I want to know how to set the initial ort_past_input to empty input. Explanation The reinterpret_cast operator can be used for conversions such as char* to int*, or One_class* to Unrelated_class*, which are inherently unsafe. Syntax : Note however that using vectorized loads increases register pressure and reduces overall parallelism. NVIDIA NPP is a library of functions for performing CUDA accelerated 2D image and signal processing. A reinterpret_cast of a pointer to a pointer does not (ie. Contribute to CNugteren/CLBlast development by creating an account on GitHub. ONNX Runtime Performance Tuning. reinterpret . Behavior of reintepret_cast of CUDA pointers? AppendExecutionProvider_CUDA (cuda_provider_options); Ort::Session session (*ort_env, MODEL_URI, session_options); // Use a run option like this while invoking Run () to trigger a memory arena shrinkage post Run () // This will shrink memory allocations left unused at the end of Run () and cap the arena growth. Better way to check if an element only exists in one array. = reinterpret_cast(arr)[offset]; EDIT: so far in my testing of both methods, there seems to not be much of a difference. Explanation Unlike static_cast, but like const_cast, the reinterpret_cast expression does not compile to any CPU instructions (except when converting between integers and pointers or on obscure architectures where pointer representation depends on its type). Dereferencing those pointers will cause the compiler to generate the vectorized instructions. C++CCC++, C++, reinterpret_castconst_castdynamic_caststatic_cast, interpretrecastC++Effective C++cast0101int32bit), Ubuntu 14.04 LTSg++ 4.8.4, 6num0x00636261numpnumreinterpret_castpnumint*char*pstr, pnumpstrreinterpret_cast 1112, pnum636261pstrabc, C++, reinterpret_castpnumint*char*pstrpstrnumpstrchar*pstrnumcharcharBytepstrapstrpstrpstrchar*\0num0x63006261,ab, pstr\0num0x64636261, abcd6, numpnumchar*, pstrpstr0x64636261, reinterpret_castreinterpret_castB, //pstrpstr. reinterpret_cast < new-type > ( expression ) Returns a value of type new-type . It's possible of course, to cast a properly aligned pointer to a type that no longer has proper alignment. in each Conv node. C++. For example, a properly aligned float pointer that is not at an evenly-divisible-by-4 float offset (index) cannot be properly cast to a float4 pointer for CUDA device usage. Convolution-heavy models and the CUDA EP ORT leverages CuDNN for convolution operations and the first step in this process is to determine which "optimal" convolution algorithm to use while performing the convolution operation for the given input configuration (input shape, filter shape, etc.) static_cast conversion C++ C++ language Expressions Converts between types using a combination of implicit and user-defined conversions. Browse Source Accept non-standard bools in more CUDA kernels This fixes all remaining CUDA kernels, except those using `cub` or `thrust`, to accept boolean tensors with values oth CUDA allocate memory in __device__ function. We can inspect the assembly for this kernel using the cuobjdumptool included with the CUDA Toolkit. reinterpret_cast is a type of casting operator used in C++. Already on GitHub? Would total execution time spent on recasting really be that significant that it actually matters, in either case? Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. Another issue is that some of the aligned types will be loaded via __ldg (). Full answer: Let's consider basic number types. It is similar in use to a plain C-style cast and generally . Why does Malloc() care about boundary alignments? Finally, we launch half as many threads as we did in the scalar kernel. infile.read(reinterpret_cast<char*>(buffer), inH * inW); } Have a question about this project? Third, we handle any remaining elements which may arise if N is not divisible by 2. It is used for reinterpreting bit patterns and is extremely low level. Find centralized, trusted content and collaborate around the technologies you use most. Others have pointed out that the standard defines different rules for the two kinds of cast. Other uses are, at best, nonportable. For example in C++ you can recast the int pointer d_in to an int2 pointer using reinterpret_cast<int2*> (d_in). As with all cast expressions, the result is: an lvalue if new_type is an lvalue reference type or an rvalue reference to function type; ; an xvalue if new_type is an rvalue reference to object type; ; a prvalue otherwise. Here, the "Add" operator from the host initiated a CUDA kernel on device named "ort_add_cuda_kernel" which lasted for 33 microseconds. OneFlowElement-Wise CUDAElement-Wise CUDA. The purpose of reinterpret_cast is to reinterpret the bits of one value as the bits of another value. Well occasionally send you account related emails. Help us identify new roles for community members, Proposing a Community-Specific Closure Reason for non-English content. Add a new light switch in line with another switch? When you convert for example int (12) to unsigned float (12.0f) your processor needs to invoke some calculations as both numbers has different bit representation. Figure 1 shows the throughput of the kernel in GB/s as a function of copy size. On CUDA 11, this is no longer required. The CUDA Execution Provider enables hardware accelerated computation on Nvidia CUDA-enabled GPUs. You may also be interested in this question. All the other instructions are the same. Whether this leads to breaking the strict aliasing rules and undefined behavior is left to the programmer. If you will need it in the future, you will know. Received a 'behavior reminder' from manager. In this post, Ive shown how you can easily incorporate vectorized loads into existing kernels with relatively few changes. should not) change the underlying numerical value (bit pattern representation) of a pointer. cudaopenglgpgpu cuda; Cuda GPUFFT cuda opencl; CUDA cuda; CUDA cuda; Cuda b40c\u cuda; CUDA*.cu cuda How could my characters be tricked into thinking they are on Mars? Second, we use the casting technique described above in the copy. reinterpret_cast followed by const_cast And you thought it is just a single evil cast, in fact its a hydra! This is the trickiest to use. 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