coalescing | 易学教程

Memory coalescing and nvprof results on NVIDIA Pascal

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问题 I am running a memory coalescing experiment on Pascal and getting unexpected nvprof results. I have one kernel that copies 4 GB of floats from one array to another one. nvprof reports confusing numbers for gld_transactions_per_request and gst_transactions_per_request . I ran the experiment on a TITAN Xp and a GeForce GTX 1080 TI. Same results. #include <stdio.h> #include <cstdint> #include <assert.h> #define N 1ULL*1024*1024*1024 #define gpuErrchk(ans) { gpuAssert((ans), __FILE__, __LINE__);

CUDA - Coalescing memory accesses and bus width

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问题 So the idea that I have about coalescing memory accesses in CUDA is, that threads in a warp should access contiguous memory addresses, as that will only cause a single memory transaction (the values on each address are then broadcast to the threads) instead of multiple ones that would be performed in a serial manner. Now, my bus width is 48 bytes. This means I can transfer 48 bytes on each memory transaction, right? So, in order to take full advantage of the bus, I would need to be able to

Coalescing while using NSNotificationQueue

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问题 I wrote the following code to perform coalescing using NSNotificationQueue.I want to post only one notification even if the event occurs multiple times. - (void) test000AsyncTesting { [NSRunLoop currentRunLoop]; [[NSNotificationCenter defaultCenter] addObserver:self selector:@selector(async000:) name:@"async000" object:self]; [[NSNotificationQueue defaultQueue] enqueueNotification:[NSNotification notificationWithName:@"async000" object:self] postingStyle:NSPostWhenIdle coalesceMask

Linux select() and FIFO ordering of multiple sockets?

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问题 Is there any way for the Linux select() call relay event ordering? A description of what I'm seeing: On one machine, I wrote a simple program which sends three multicast packets, one to each of three different multicast groups. These packets are sent back-to-back, with no delay in between. I.e. sendto(mcast_group1); sendto(mcast_group2); sendto(mcast_group3). On the other machine, I have a receiving program. The program uses one socket per multicast group. Each socket does a bind() and IP_ADD

Does vector<atomic_bool> involves coalescing vector elements?

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问题 As stated in the subject: does vector<atomic_bool> involves coalescing vector elements in the same way of vector<bool> ? 回答1: No. std::vector has only one specialization, std::vector<bool> . bool and std::atmoic_bool are two different types and as a result std::vector<atomic_bool> will work like other std::vector<T> of type T . 来源： https://stackoverflow.com/questions/57110670/does-vectoratomic-bool-involves-coalescing-vector-elements

CUDA coalesced access to global memory

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问题 I have read CUDA programming guide, but i missed one thing. Let's say that i have array of 32bit int in global memory and i want to copy it to shared memory with coalesced access. Global array has indexes from 0 to 1024, and let's say i have 4 blocks each with 256 threads. __shared__ int sData[256]; When is coalesced access performed? 1. sData[threadIdx.x] = gData[threadIdx.x * blockIdx.x+gridDim.x*blockIdx.y]; Adresses in global memory are copied from 0 to 255, each by 32 threads in warp, so

Timer Coalescing before Windows 7

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问题 There is timer coalescing support in Windows 7 and Windows 8, see for example this: Timer coalescing in .net Windows 7 has a function SetWaitableTimerEx about which it is claimed that it supports coalescing here and here. Windows 8 has additionally a function SetCoalescableTimer which supports coalescing according to MSDN. So lots of talk about timer coalescing in Windows 7 and Windows 8. But then it seems like it may have been implemented already earlier. Is it so? First, is it correct that

CUDA programming - L1 and L2 caches

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Could you please explain the differences between using both "L1 and L2" caches or "only L2" cache in CUDA programming? What should I expect in time execution? When could I expect smaller gpu time? When I enable both L1 and L2 caches or just enable L2? thanks Typically you would leave both L1 and L2 caches enabled. You should try to coalesce your memory accesses as much as possible, i.e. threads within a warp should access data within the same 128B segment as much as possible (see the CUDA Programming Guide for more info on this topic). Some programs are unable to be optimised in this manner,

CUDA programming - L1 and L2 caches

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问题 Could you please explain the differences between using both "L1 and L2" caches or "only L2" cache in CUDA programming? What should I expect in time execution? When could I expect smaller gpu time? When I enable both L1 and L2 caches or just enable L2? thanks 回答1: Typically you would leave both L1 and L2 caches enabled. You should try to coalesce your memory accesses as much as possible, i.e. threads within a warp should access data within the same 128B segment as much as possible (see the

CUDA - Coalescing memory accesses and bus width

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So the idea that I have about coalescing memory accesses in CUDA is, that threads in a warp should access contiguous memory addresses, as that will only cause a single memory transaction (the values on each address are then broadcast to the threads) instead of multiple ones that would be performed in a serial manner. Now, my bus width is 48 bytes. This means I can transfer 48 bytes on each memory transaction, right? So, in order to take full advantage of the bus, I would need to be able to read 48 bytes at a time (by reading more than one byte per thread - memory transactions are executed by a