cuda reinterpret_cast

For example in C++ you can recast the int pointer d_in to an int2 pointer using reinterpret_cast<int2*> (d_in). If you will need it in the future, you will know. reinterpret . This version of the code has reduced the instruction count by a factor of 4. Many CUDA kernels are bandwidth bound, and the increasing ratio of flops to bandwidth in new hardware results in more bandwidth bound kernels. advantage to using reinterpret_cast . Notice that now the compiler generates LD.E.64 and ST.E.64. On CUDA 11, this is no longer required. 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. should not) change the underlying numerical value (bit pattern representation) of a pointer. How big is the array, and how many times do you recast (the array)? Well occasionally send you account related emails. Connect and share knowledge within a single location that is structured and easy to search. 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. Third, we handle any remaining elements which may arise if N is not divisible by 2. ONNX Runtime Performance Tuning. Not sure if it was just me or something she sent to the whole team. 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 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.) Already on GitHub? Please reference Install ORT. Making statements based on opinion; back them up with references or personal experience. This is the trickiest to use. . reinterpret_cast < new-type > ( expression ) Returns a value of type new-type . Why would Henry want to close the breach? Not the answer you're looking for? privacy statement. Add a new light switch in line with another switch? All the other instructions are the same. In almost all cases vectorized loads are preferable to scalar loads. GCC allows this code, but I would expect/hope that compilation would succeed with the (in this case) more conformant clang compilation rules. Better way to check if an element only exists in one array. Are there breakers which can be triggered by an external signal and have to be reset by hand? First, the loop now executes only N/2 times because each iteration processes two elements. Why does the USA not have a constitutional court? 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. . 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. = reinterpret_cast(arr)[offset]; EDIT: so far in my testing of both methods, there seems to not be much of a difference. 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. It is similar in use to a plain C-style cast and generally . 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. I will load onnx model using ONNX Runtime C++ API. 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? 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. Help us identify new roles for community members, Proposing a Community-Specific Closure Reason for non-English content. The result of a reinterpret_cast cannot safely be used for anything other than being cast back to its original type. Finally, we launch half as many threads as we did in the scalar kernel. static_cast conversion C++ C++ language Expressions Converts between types using a combination of implicit and user-defined conversions. For each model running with each execution provider, there are settings that can be tuned (e . ONNX Runtime Performance Tuning. Why is the eastern United States green if the wind moves from west to east? About Pointer alignment, is there a way to make a pointer aligned to some given memory boundary? veca = reinterpret_cast<int4*>(&a[1])[0]; Suggestion: run your code with cuda-memcheck. Hebrews 1:3 What is the Relationship Between Jesus and The Word of His Power? The reinterpret_cast operator can be used for conversions such as char* to int*, or One_class* to Unrelated_class*, which are inherently unsafe. 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) { //. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Dereferencing those pointers will cause the compiler to generate the vectorized instructions. rev2022.12.9.43105. cudaopenglgpgpu cuda; Cuda GPUFFT cuda opencl; CUDA cuda; CUDA cuda; Cuda b40c\u cuda; CUDA*.cu cuda For each model running with each execution provider, there are settings that can be tuned (e . Therefore whatever alignment conditions exist will not be affected by that kind of cast. Find centralized, trusted content and collaborate around the technologies you use most. It does not check if the pointer type and data pointed by the pointer is same or not. Asking for help, clarification, or responding to other answers. Second, we use the casting technique described above in the copy. I just want to make sure about that since some of the functions I wrote depend on it. Note however that using vectorized loads increases register pressure and reduces overall parallelism. Behavior of reintepret_cast of CUDA pointers? Another issue is that some of the aligned types will be loaded via __ldg(). We can improve performance of this operation by using the vectorized load and store instructions LD.E. 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). The C++ compiler detects and quietly fixes most but not all violations. Others have pointed out that the standard defines different rules for the two kinds of cast. Why does Malloc() care about boundary alignments? NPP will evolve over time to encompass more of the compute heavy tasks in a variety of problem domains. In other words will it be fundamentally the same if I do this; (within kernel with arr as float(assuming correct indexing is done) ). NVIDIA NPP is a library of functions for performing CUDA accelerated 2D image and signal processing. reinterpret_cast is a tricky beast. 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. This kernel has only a few changes. So if you have a kernel that is already register limited or has very low parallelism, you may want to stick to scalar loads. Here we can see the generated LD.E.128 and ST.E.128. in each Conv node. Explanation in each Conv node. Would total execution time spent on recasting really be that significant that it actually matters, in either case? You can debug python convert_to_onnx.py -m gpt2 -o to see how the function is used in inference.. We can also write a vector4 version of the copy kernel. Another issue is that some of the aligned types will be loaded via __ldg (). How did muzzle-loaded rifled artillery solve the problems of the hand-held rifle? ONNX Runtime provides high performance across a range of hardware options through its Execution Providers interface for different execution environments. A reinterpret_cast of a pointer to a pointer does not (ie. So, in our case, the inputs are an integer giving the dimension of the problem . 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. {64,128} and ST.E.{64,128}. This is the function that we want to expose to JAX. 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.) 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. How to smoothen the round border of a created buffer to make it look more natural? 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. Why does the distance from light to subject affect exposure (inverse square law) while from subject to lens does not? Sign in How could my characters be tricked into thinking they are on Mars? 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. The primary set of functionality in the library focuses on image processing and is widely applicable for developers in these areas. Forcing a conversion that could happen anyway: double d = 4.5; I usually recommend that for everyone, before asking for help. 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. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. What is the difference between (void **)&x and (void *)x? The short answer: If you don't know what reinterpret_cast stands for, don't use it. It's possible of course, to cast a properly aligned pointer to a type that no longer has proper alignment. 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. It is important to remember that even though a program compiles, its . Here, the "Add" operator from the host initiated a CUDA kernel on device named "ort_add_cuda_kernel" which lasted for 33 microseconds. However, it is important to note that there will be half as many instructions executed because the loop only executes N/2 times. Environment details (please complete the following information): Additional context . We do not currently allow content pasted from ChatGPT on Stack Overflow; read our policy here. 0x1. In this post, Ive shown how you can easily incorporate vectorized loads into existing kernels with relatively few changes. 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. For example in C++ you can recast the int pointer d_in to an int2 pointer using reinterpret_cast(d_in). The text was updated successfully, but these errors were encountered: Successfully merging a pull request may close this issue. 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. In C99 you can do the same thing using the casting operator: (int2*(d_in)). A reinterpret_cast of a pointer to a pointer does not (ie. Syntax : Tuned OpenCL BLAS. Received a 'behavior reminder' from manager. 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. 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). To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Now that we have seen how to generate vectorized instructions lets modify the memory copy kernel to use vector loads. When does casting change a value's bits in C++? This 2x improvement in instruction count is very important in instruction-bound or latency-bound kernels. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. 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 C++reinterpret_cast. The CUDA Execution Provider enables hardware accelerated computation on Nvidia CUDA-enabled GPUs. // 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. V Jyothi. 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. ONNX Runtime provides high performance across a range of hardware options through its Execution Providers interface for different execution environments. Can you give me some advice? reinterpret\u cast "" @255 Along with this flexibility comes decisions for tuning and usage. Vectorized loads are a fundamental CUDA optimization that you should use when possible, because they increase bandwidth, reduce instruction count, and reduce latency. Syntax static_cast < new-type > ( expression ) Returns a value of type new-type . 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 reinterpret_cast < new-type > ( expression ) Returns a value of type new-type . Full answer: Let's consider basic number types. Other uses are, at best, nonportable. infile.read(reinterpret_cast<char*>(buffer), inH * inW); } Using vectorized loads reduces the total number of instructions, reduces latency, and improves bandwidth utilization. Contribute to CNugteren/CLBlast development by creating an account on GitHub. to your account. 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. You can safely offset arrays if you use an aligned offset, as inreinterpret_cast(d_in+2). reinterpret_cast is a type of casting operator used in C++. Have a question about this project? CUDA allocate memory in __device__ function. These operations also load and store data but do so in 64- or 128-bit widths. reinterpret_cast is usually used for casting unrelated types. You can easily use these types via type casting in C/C++. GELU dtype = float32shapeNVIDIA A100-PCIE-40GB. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. reinterpret_cast followed by const_cast And you thought it is just a single evil cast, in fact its a hydra! You signed in with another tab or window. Implementing realloc in CUDA without moving data. Penrose diagram of hypothetical astrophysical white hole. In this code, I am using grid-stride loops, described in anearlier CUDA Pro Tip post. Expected behavior How can I use a VPN to access a Russian website that is banned in the EU? Thanks for contributing an answer to Stack Overflow! We can inspect the assembly for this kernel using the cuobjdumptool included with the CUDA Toolkit. Some CUDA pointers need to be naturally aligned. You can also generate vectorized loads using structures as long as the structure is a power of two bytes in size. Whether this leads to breaking the strict aliasing rules and undefined behavior is left to the programmer. reinterpret_cast. Updated on 23-Jun-2020 13:57:11. My way of describing static_cast is that it supports two functions: 1. Along with this flexibility comes decisions for tuning and usage. [] 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 . 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. 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. Figure 1 shows the throughput of the kernel in GB/s as a function of copy size. Therefore whatever alignment conditions exist will not be affected by that kind of cast. By clicking Sign up for GitHub, you agree to our terms of service and This makes it very important to take steps to mitigate bandwidth bottlenecks in your code. C++. To learn more, see our tips on writing great answers. Inspecting the SASS we see the following. 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. The purpose of reinterpret_cast is to reinterpret the bits of one value as the bits of another value. Ready to optimize your JavaScript with Rust? 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. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Requirements It's possible of course, to cast a properly aligned pointer to a type that no longer has proper alignment. . Is it appropriate to ignore emails from a student asking obvious questions? Describe the bug For example reinterpret_cast(d_in+1) is invalid because d_in+1 is not aligned to a multiple of sizeof(int2). 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It is used for reinterpreting bit patterns and is extremely low level. In cuda_memcmp, which is declared constexpr, two reinterpret_cast calls appear, as shown here. 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 () { should not) change the underlying numerical value (bit pattern representation) of a pointer. 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. Will there be any performance difference(optimizations) with using reinterpret_cast within the kernel vs. casting in the kernel call from host? Lets begin by looking at the following simple memory copy kernel. This requires compilation with clang. 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. If an operator called multiple kernels during execution, the performance numbers of those kernels will all be listed following the call sequence: I discovered this while trying to set up IWYU for cuml (and hopefully other RAPIDS projects eventually). Should I give a brutally honest feedback on course evaluations? You can see the overall performance for all 3 kernels in Figure 2. Remove constexpr from bitwise_compare functions. OneFlowElement-Wise CUDAElement-Wise CUDA. reinterpret_cast <cuDoubleComplex*>(c), ldc, stridec, num_batches));} template <> . 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