Nvprof no output

  • Unfortunately due to non-buffered output nvprof results will be mixed and will have no information about How to use NVIDIA profiler. g. 14s, over 30x faster than the serialized version. 1. Unfortunately, there’s no way to force nvprof to flush the data it collected to disk, so for CUDA profiling one has to use this context manager to annotate nvprof traces and wait for the process to exit before inspecting them. Nvprof has no concept of output pages, all data is shown as a list or summarized. There isn't really a specific kernel I'm debugging—it's a 70k-line project that I've started to look at (closed source unfortunately) and the memory leak could be hiding almost anywhere. What happens when unified memory is accessed only by the CPU? What happens when unified memory is accessed only by the GPU? Reducing timeline trace output file size¶ If the application is already recording the HIP APIs, the HSA APIs are somewhat redundant and the ATP file size can be substantially reduced by not recording these APIs. /sumMatrix dimX dimY a is output GPU is more suitable for such E6895 Advanced Big Data To GPU or Not to GPU Sumit Jamgade Cloud Developer. Detailed it from nvprof. 1, including nvprof – Profile Data Import Produce profile into a file using –o Import into Visual Profiler —File menu -> Import nvprof Profile… Import into nvprof to generate textual outputs $ nvprof –o profile. In contrast to the Nsight IDE, we can freely use any Python code that we have written—we won't be compelled here to write full-on, pure CUDA-C test function code. I. Update: It makes sense that using only odd grain sizes would not help. This is the number one reason why I figured out how to do this. If for any reason two intervals intersect only partialy, this function will not record . inp -o test. /a. out $ nvprof –i profile. . For example, you can dump statistics like this with nvprof: 23 NVTX – Our current tools only profile API calls on the host – What if we want to understand better what the host is doing? – The NVTX library allows us to annotate profiles with ranges If you read the motivation to this article, the secret is already out: There is yet another type of read-only memory that is available for use in your programs written in CUDA C. On the server, you can use the "eog" image viewer to check the output (see the command below), assuming that you have enabled the X tunneling via ssh. *. Focus is the yellow text, the GPU activities. GitHub Gist: instantly share code, notes, and snippets. I ran your nvprof --analysis-metrics command on the cuda vectorAdd sample. If there is a certain number of kernels you wish to profile, you can select the checkbox After skipping N kernels, profile X kernels. kernels running on NVIDIA GPUs, no matter what language they are  aprun -n 15 -b nvprof --profile-child-processes . The nvprof is a light-weight profiling tool which enables you to collect and view profiling data from the command-line. cuda-emr. In summary mode, each range is shown with CUDA activities associated with that range. Improving CUDA profiler output of the MPI-CUDA program. For example, if I know I’m moving 1GB, and my kernel takes 10ms, I expect the profiler to report 100GB/s. The other day I went to use the new nvprof command line profiler and was greeted with the following error: NVPROF Many New Metrics: - Tensor Core Metrics - L2 Metrics - Memory Instructions Per Load/Store Display PCIe Topology View Trace and Profile in combined output (--trace) VISUAL PROFILER Summary View for Memory Hierarchy Improved Handling of Segments for UVM Data on the Timeline 3. Allowed values: "flat" - Show flat profile "top-down"  Profiling tools (e. That is, each SM can support no more than 1536 threads that can run in parallel. /my-cuda-mpi-app Prevent MPI runtime from forking the app into a separate process, which hides it from nvprof Unique profile output file per MPI rank output out features input features input features batch Fully Connected / Dense / Linear (PyTorch nn. 3. If you are importing multiple the PGI Profiler output files into the session, it is environment variable will not be available in the shell where nvprof is running. 1 has been released with the following features and improvements: General. A modern Intel i7 CPU can hit almost I'm trying to train a model which in my opinion is taking too long compared to other datasets given that it's taking about 9s to complete a step. Here is my code: #include <iostream> #include <math. /nvprof -m tensor_precision_fu_utilization . The word supercomputer gets thrown around quite a bit. py --network mlp --num-epochs 1. The largest subgraph that can be created is shown in ©. Only dig deep into a kernel if it’s taking a significant amount of your time. We will end with a brief overview of the command-line Nvidia nvprof profiler. 5 | vi Terminology An event is a countable activity, action, or occurrence on a device. 2018-results/bgs I recently implemented Successive Over Relaxation using Cuda as a part of my course project and was curious to know how I can make the code more efficient. nvprof is a command-line profiler available for Linux, Windows, and OS X. It has to be remembered that there is no capping on the number of thread blocks that can occupy a SM. txt. - The NVIDIA command-line profiler, nvprof, now supports collection of any number of events and metrics during a single run of a CUDA application. HIP includes a text file that lists all of the HSA APIs and can assist in this filtering: The NVIDIA Deep Learning Institute (DLI) offers hands-on training in AI and accelerated computing to solve real-world problems. I am aware of https://devblogs. 19 Sep 2015 I would understand :D. I have the latest CUDA toolkit and drivers installed on a 12. 06. How do you get a detailed Kernel profile using nvprof from the command line in Linux? You may use Nsight Eclipse Edition on Linux if commandline is not a affecting the optimization level of the output, the -lineinfo option to nvcc can be. Unfortunately I don't have any example output around, but it can be used like this: nvprof \ --devices 0 \ --metrics l1_cache_global_hit_rate \ --metrics shared_load_transactions \ . My operating system is Ubuntu 12. Perhaps you'll need to be more specific about your exact test case and the exact places you are looking for data. Readers familiar with the workings of graphics hardware will not be surprised, but the GPU’s sophisticated texture memory may also be used for general-purpose computing. nv Exam the profiler's output in ~/nvprof. PPCG changed parts of it's API, so update PPCGCodeGeneration to adapt; to the changes. 04. It is often helpful to check the execution time of each operation in a neural network. 00GB/s, Physical Links 2, Sysmem Access True, Sysmem Atomic Access False, Peer Access False, Peer Atomic Access False nvprof can print nice statistics if you put the appropriate switches. Unfortunately due to non-buffered output nvprof results will be mixed and will have no information about Improving CUDA profiler output of the MPI-CUDA program. I have run some benchmark to get the power, What I don't understand is that is the result of BlackscholesGPU + CUDA You can open your output file in Nvidia Visual Profiler (usually included in CUDA SDK). 00 Loop not vectorized: data dependency Loop unrolled 4 times  It's probably due to a not correctly set $LD_LIBRARY_PATH. Low utilization of memory compute bound. cuBLAS Library ‣ A license is no longer required in order to use cuBLAS-XT with more than two GPUs. If you have a long running kernel or set of kernels with no explicit synchronisation to the host, and follow them with a call to cudaMemcpy, the cudaMemcpy call will be launched 3. /your_app In case no filter is set at all or the specified filter is invalid, all kernel launches for all kernels will be profiled. /myprogram > > Now I'd like to give nvprof an argument where to store the output files. There are however still 100 transfers from device to host (GPU to CPU): every time we call sess. Fig 9 Output of nvprof in the command I am trying to profile my CUDA program, using the nvprof tool. You can then determine where to focus your effort to speed up model training or inference. NVIDIA Nsight Compute CLI uses pages to define how data should be structured and printed. 5. To debug the kernel, you can directly use printf() function like C inside cuda kernel, instead of calling cuprintf() in cuda 4. /cp2k. nvvp --analysis-metrics . It corresponds to a single hardware counter value which is collected during kernel execution. Skip to content. pgm". INFO:root:start with  Zero means no limit. Nvprof is a handy tool. 16. The elements of x are incremented by one in GPU. This is a very ugly way to finish profiling, but cudaProfilerStop() on its own didn't produce any output and neither did the addition of exit(0). In this paper, we investigate this The only constraint is that the subgraph should be a direct cyclic graph and have no loops. Output of (except for tables) are prefixed with ==<pid>==, <pid> being the process ID of the application being profiled. Example: The visual PGI Profiler does not require any application changes; however, . EXT, where XXXX is the family number, YY is the bin number, and EXT is the output file type extension. /custom-relax-eps-cuda-default. Single-Assignment C Group Repositories. log is your output file name). This environment variable should include the path to the CUDA library. nvprof • Nvprof also gives topology information • Example: nvprof --print-nvlink-topology . GitHub is home to over 36 million developers working together to host and review code, manage projects, and build software together The difference in total time is due to the fact that work is launched to the GPU asynchronously. cuda-toolkit-x. A programming model, Unified Memory Access (UMA), has been recently introduced by Nvidia to simplify the complexities of memory management while claiming good overall performance. , when to use local/shared/global memory. 2. log This one works if executed in the command line. out: + srun --partition=debug -n 1 -C gpu nvprof -f --export-profile standalone-nvprof-output. txt ; Frequently asked To give some weight to what I said we will take a brief look at Nvidia’s nvprof command line profiler. Also, it is not straightforward to calculate the flops of a kernel (especially long kernels, other mathematical functions like cos, sin, etc which have more flop counts). An example profile for a linear scaling benchmark (TiO2) is shown here To run on CRAY architectures in parallel the following additional tricks are needed NVPROF • Collect Performance events and metrics <program output> ==21982== Generated result file: timeline. As this can pose a high performance overhead, a warning icon will be displayed. y/bin/nvprof -o output. If you work with CUDA programs, you will use the Visual Profiler regularly. Unique profile output file per MPI rank  We will end with a brief overview of the command-line Nvidia nvprof profiler. Using the vecaddmod example from the openacc getting started guide (corrected so that it compiles) S7445 -WHAT THE PROFILER IS TELLING YOU OPTIMIZING WHOLE APPLICATION PERFORMANCE. Here we will test to see if the file exists, if it doesn't then Use nvprof to figure out where your bottleneck is. There's also one more possibility to produce human-readable files: you can specify --log-file human-readable-output. Example: Without cudaProfilerStart/ Stop. 0704-0188 The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining In the previous chapter, It has already been discussed that the current generation of CUDA devices support upto 1536 thread slots. nvprof -o log. NVIDIA Development Tools Solutions - ERR_NVGPUCTRPERM: nvprof Permission issue with Performance Counters . I am looking for a program that can monitor the PCI Express bus usage on Linux. CUDA Installation - Learn CUDA in simple and easy steps starting from basic to advanced concepts with examples including Introduction, Introduction to the GPU, Fixed Functioning Graphics Pipelines, Key Concepts, Keywords and Thread Organization, Installation, Matrix Multiplication, Threads, Performance Considerations, Memories, Memory Considerations, Reducing Global Memory Traffic, Caches. The CUDA profiling tools do not require any application changes to enable . YY. out. Low utilization of compute + memory no parallelism. Be sure to record your hypotheses, as well as the results, obtained from nvprof output, specifically CPU and GPU page faults, for each of the 4 experiments you are conducting. GitHub Gist: star and fork rharriso's gists by creating an account on GitHub. PPCG now uses isl_multi_pw_aff instead of an array of pw_aff. $nvidia-smi 2. I had no trouble with analysis data both in the analysis tab and details tab of the visual profiler. The easiest to begin with is nvprof , a command-line profiler available  The debugging tools for windows on production systems. , what kind of metrics to collect). nvidia. The runtime can be obtained directly from nvprof ( with --print-gpu-trace or --print-api-trace command). Low utilization of compute memory bound. /app_name. I try to do the same with > python and mpi4py. h> #include <cuda_profiler_a The XRay Image query was enhanced to support output file familying. Anumber of switches enable control over the profiling process (e. I am mostly interested in the PCI Express bus usage between an Nvidia GPU and CPU. At first glance, nvprof seems to be just a GUI-less version of the graphical profiling features available in the NVIDIA Visual Profiler and NSight Eclipse edition. 2. Many useful metrics, some good ones are acheived_occupancy, ipc, shared_replay_overhead, all of the utilizations, all of the throughputs. To see a list of all available events on a particular NVIDIA GPU, type nvprof --query-events. Being pushed by NVidia, through its Portland Group division, as well as by Cray, these two lines of compilers offer the most advanced OpenACC support. This needs us to adjust how we index array bounds and how we construct array bounds. > May I ask why do you want to use mpi4py for such a Management and Monitoring Interfaces for GPU Devices in a Cluster Environment Sadaf Alam 2014 HPC Advisory Council Meeting, Lugano, Switzerland Profiling If we time it using nvprof profiler, we can see that there are only 5 host to device transfers (i. I have mvn installed in /tools/noarch/apa CUDA 6. Another tool that can be useful is the commandline profiler, named nvprof. List all profiler events with nvprof --query-events and all metrics with nvprof --query-metrics. 2 Using nvprof Embed MPI rank in output filename, process name, and context name NVIDIA® Nsight™ Compute 2019. The Debugging This component is non-intrusive – it has no effect on the video data going through the stream. /matrix -n 1024 -N 1024 -m cosimple (nvprof has many other options) Visualize profile with nvvp cosimple. Where possible, try to match profiler output with theory For example, if I know I’m moving 1GB, and my kernel takes 10ms, I expect the profiler to report 100GB/s. The video trace . /app_name • Output : Graphics Device 1 port 0, 1, CPU, Nvlink Bandwidth 40. exe of The Command Line Profiler and nvprof are mutually exclusive, so COMPUTE_PROFILE must be set to 0 when we use nvprof. It might be worthwhile to change the code so that output label sizes can be specified, as this controls which template instantiation gets called. Incontrast with the debugging process, there is no mandate for using two GPUs, so profiling a remote system is just a In particular, the NVIDIA Visual Profiler allows you to load the output of nvprof for analysis, allowing you to profile on a remote machine, but analyze locally. log filterfix3-05. The original Cray-1, for example, operated at about 150 MIPS and had about eight megabytes of memory. There are a few question on the site that seem related to my problem but I could not find a solution in any of them. log option for nvprof (of course human-readable-output. Here is the result of the only -1 results (from my initial run without --ipc=host or --privileged): only_negative_1. When file familying is enabled the output files are named outputXXXX. out <app> <app args> $ nvprof –i profile. /mycode. 18 Form Approved OMB No. sh), returns . Tools to analyze the program statically: 1. There are other nodelets running other CUDA code on both the same and different GPUs, so perhaps we needed to force every CUDA process to stop to get the profiling results to output? nvprof --metrics gld_throughput,gld_efficiency,achieved_occupancy . 04 server. nvprof is a command-line-based utility for profiling CUDA programs. If we look at the output of nvprof we can get a bit more detail. Profiling With nvprof and the NVIDIA® Visual Profiler. This does not have as many features of the Visual Profiler, but is very easy and quick to use. 22 COMMUNICATION + COMPUTATION OVERLAP 4/24/2 017 This is a bit more work than the serialized version, and way less readable than a well written C++ program. /standalone srun: job 4739428 queued and waiting for resources srun: job 4739428 has been allocated resources Multi-GPU Programming Supercomputing 2011 – Note that no link has 2 communications in the same “direction” // swap input/output pointers } } Run “nvprof” to generate analysis data nvprof --analysis-metrics -f -o cosimple. An example of nvvp output: https://developer. --cpu-profiling-mode <mode> Set the output mode of CPU profiling. The GPU is unresponsive on a CSEClass machine. The nvprof and nvvp output is a very good place to start. aprun launches processes on compute nodes. sopt -i test. An example of nvprof output: How do you get a detailed profile of CUDA kernel? How do you get a detailed Kernel profile using nvprof from the command line in Linux? affecting the optimization level of the output, the Profiler User's Guide DU-05982-001_v5. It is the tool of choice for profiling remote systems. This returns the utilization level of the multiprocessor function units executing Tensor cores instructions on a scale of 0 to 10. prof . pdf from ECE 8823 at Georgia Institute Of Technology. sjamgade@suse. Introduction to OpenACC Peng Wang HPC Developer Technology, NVIDIA Always use “-Minfo=accel” and pay attention to the output! $ nvprof . so for CUDA profiling one has to use this context manager to annotate nvprof   NVPROF - a command line text-based version of the NVIDIA Visual Profiler. Once again using our multidimensional increment code, we obtain the following output when executing nvprof . 0 RN-06722-001 _v7. Output of nvprof. Using nvprof with theano code on windows cmd - no profiling data recorded nvprof doesn't output any profiling information and gives a warning: Some profiling data View 0207_Intro-to-nvprof. Is the juice worth the squeeze? On my hardware, this is a dramatic yes. Follow. nvprof No overheads in kernel runtime, CPU Luckily there is a workaround for that. needed to inform the compiler to use the directives, there will be no output:. Support for CUDA Toolkit 10. 3 and run the . The drivers are working fine: all the NVIDIA sample code compiles and runs and I've written, compiled, and run several CUDA programs. The timeline is a good place to go next. So I am using Nsight Systems 2019. Note that profiling of metric and event is only supported up to the Volta architecture through Nvprof. Aside from that caveat, using nvprof is as simple as running it with your CUDA application command as an argument. I'm using Red/Black SOR scheme which is NVIDIA CUDA Toolkit v7. com/nvidia-visual-profiler . 25% Intensity = 1. nvprof You will want to run this locally so X-windows doesn’t lag CMU 15-418/15-618, Spring 2019 # OK, now if we had a UNC path, nvcc has a tendency to only output the first '/' # instead of '//'. This Managing memory between the CPU and GPU is a major challenge in GPU computing. All gists Back to GitHub. In my experiments, there was no improvement. out ==3744== Profiling application: . Markers and ranges are shown in the API trace output in the timeline. In the next part of this post, I will introduce the basic usage of nvprof. %q{ALPS_APP_PE}. where, time is the runtime of the kernel in seconds. The detected speed signs are annotated in an output image called "output. $cuobjdump -res-usage app ptxas . Some useful events are global_ld_mem_divergence_replays, global_st_mem_divergence_replays, shared_load_replay, Thanks, those links gave me a better understanding of VTune's features! I guess I'll try the free trial. Z39. log +32-0 cuda-noemr. txt ; Frequently asked questions. E. This post > describes how I could do it with a shell script. nvprof also supports NVTX markers and ranges. Those correspond to the report pages used in the GUI variant. Any kernel showing a non-zero value is using Tensor cores. Nsight Eclipse Edition ‣ Cross compiling to the POWER8 target architecture using the GNU tool-chain is now supported within the Nsight IDE. But at the least, it proves that register blocking won’t be a magic bullet. Aside: nvprof and MPI PMI_NO_FORK=1 \ aprun nvprof \-o timeline. The only way is to create a standalone and self-contained CUDA app. When multiple users run on a device simultaneously, results may be undefined; the GPU may hang. However, I noticed that there is a limit of trace to print out to the stdout, around 4096 records, thought you may have N, e. nvprof reports “No kernels were profiled”¶ When using the nvprof tool to profile Numba jitted code for the CUDA target, the output contains No kernels were profiled but there are clearly running kernels present, what is going on? How do I save the output of a command to a file? Is there a way without using any software? I would like to know how. nvprof . 0 | 5 2. It takes the output of the batched factorization routines cublas{T}getrfBatched to compute the solution given the provided batch of right-hand-side matrices. com I want to capture to a file the output of the ls command ls >> lsOutput. > $ mpiexec -n 2 nvprof . . Basic Usage. The Cuda version finishes in around 0. CUDA it's not for everyday use but can be very powerful. * Android CUDA APK profiling not . CUDA 5 added a powerful new tool to the CUDA Toolkit: nvprof. I get "No kernels profiled" as seen shown below. Say I want to profile my kernel networks inference application. Where possible, try to match profiler output with theory. nvprof output is: Host to Device (bytes). ▫ Old environment variable based command-line profiler still available. and visualize log. 23 Mar 2017 At the first iteration we will store all data, but on next runs, we will only update running time, not definitions itself. You can use NVProf to collect time spent in a kernel you are interested in by executing build/bin/hpgmg-fv 6 8 output: Time(%) Time Calls Avg Min Max Name  nvprof. 0. cgi . CPU to GPU) as expected. added to cuBLAS. run in Tensorflow, after the computation graph is executed all the tensors that were requested are brought back to CPU (and each tensor brought back to CPU takes 1 That is, the output of your program is the approximate MAXIMUM of the continuous function f(x) (no X11 support) • nvprof – either output to screen – or to Profiling MXNet Models¶. I think that the problem is because the dataset is Using nvprof+NVVP Embed MPI rank in output filename, process name, and context name No overlap Ideal. nsight, nvvp, nvprof) . Each rank must output to separate file. ==3744== NVPROF is profiling process 3744, command: . Linear, TensorFlow swaps A and B) activation filter out batch x image height x image width input channels x filter height x filter width input channels x filter height x filter width output channels Convolution (implicit GEMM algorithm, Using the NVIDIA nvprof profiler and Visual Profiler. out ==3744 Programming Lab #2: High Performance Matrix Multiplication on a GPU There is no qlogin command, Exam the profiler's output in ~/nvprof. You can import its output into the NVIDIA NSight Visual Profiler for Eclipse available with the Android Works package. 50K, threads running on the device. 0 introduced Unified Memory, a new feature that makes it easier to write CUDA programs by automating data transfers between the CPU and GPU. A key problem with any automated data transfer solution is that it can introduce redundant transfers. Region of Interest . We see that this is very similar to the output of the Python cProfiler module that we  3 Jun 2019 The output is a TensorFlow graph with supported subgraphs replaced Setting this value to 3 (default value) would not generate TensorRT engines for . Default is zero. nvprof \. e. Furthermore, when file familying is enabled, the query will not overwrite existing files. Of > course I have to specify a separate filename for each MPI rank. nvprof with the nvvp tool, which might take several minutes to open the data. x <Program output > ======== CPU profiling result (bottom up): 84. ▫ Launch nvprof   nvprof can be used in batch jobs or smaller interactive runs; NVVP can either -- csv --log-file FILE: Generate CSV output and save to FILE; handy for plots or can be installed on your local machine; even on a laptop without an NVIDIA GPU. out --print-gpu-trace $ nvprof –i profile. But when put inside a shell script (lsOutput. nvprof reports “No kernels were profiled”¶ When using the nvprof tool to profile Numba jitted code for the CUDA target, the output contains No kernels were profiled but there are clearly running kernels present, what is going on? As far as I am aware I should be able to profile an openacc application using nvprof, but whenever I attempt to profile an application nvprof reports that no kernels were profiled. out --print-api-trace The nvprof output is a very good place to start The timeline is a good place to go next Only dig deep into a kernel if it’s taking a significant amount of your time. CMU 15-418/15-618, Spring 2019 - The NVIDIA Visual Profiler and the command-line profiler, nvprof, now support metrics that report the floating-point operations performed by a kernel. Any idea? $ nvprof --print-gpu- trace python train_mnist. The SDK includes the nvcc CUDA C/C++ compiler, the nvprof and NSight profiling . 2018-results/bgs-eigen-1000/cuda-emr. If the application is accelerated on a GPU via OpenACC the view will also contain OpenACC events and the file and line number where the OpenACC region is defined. Using the "-asm=<file_path>", I can get iocXX to spit out an assembly file from an OpenCL compilation but it looks like when there is a multi-kernel OpenCL file, only one of the kernels is written to the assembly file. How to Understand output of nvprof? Reply. cuFFT Library REPORT DOCUMENTATION PAGE Standard Form 298 (Rev. Through self-paced online and instructor-led training powered by GPUs in the cloud, developers, data scientists, researchers, and students can get practical experience and earn a certificate of competency to support professional growth. /lsOu I am trying to check whether my kernel and memcpy runs concurrently on two GPUs or not. ▫ NVIDIA® Nsight™ Visual Studio Edition. 18 Aug 2017 To add, when I cut the loop in my program short to prevent an out of memory error , I also observe cuda-memcheck not producing an output. This must be deployed onto the Shield (manually, unfortunately) and profiled using nvprof. The problem size profiled here (32 threads) is far smaller than would ever be run on the GPU. The profiler result of the manual memory usage sample is shown first. You create an import session from the output of nvprof by using the Import 23 Oct 2013 nvprof is a command-line GPU profiler for programs that use any language or and memory copies that it used, as shown in the following sample output. For cray systems, you run aprun from a login node. out As of May 2016, compiler support for OpenACC is still relatively scarce. Questions will be to this section as the need arises. 0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads Stack Exchange network consists of 175 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. Here is only the class definition  data access between CPU and GPU without explicit memory . Actually, Nvidia has provided official tools to do applications profiling. Memory is often key. 8/98) Prescribed by ANSI Std. ‣ A license is no longer required in order to use cuBLAS-XT with more than two GPUs. CUDA Libraries 2. 4 Feb 2018 In particular, the NVIDIA Visual Profiler allows you to load the output of nvprof for analysis, allowing you to profile on a remote machine, but  15 Jun 2016 nvprof: collect (or view) profiling data aprun nvprof \ ~No run time overhead. nvprof no output

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