Coming back to this discussion after a long time let me clarify a few issues that you have addressed.
1. Yes, the list of communicators in G is ordered in the same way on all
processes.
2. I am now using "mcComm != MPI_COMM_NULL" for participation check. I have
not seen much improvement but it's more elegant.
3. I have tried many things for barriers.
a) For the Bcast() case, first I tried this:
*for (unsigned int activeId = 1; activeId <= conf->getNumReducer();
activeId++){ *
* //...*
* for (auto nsit = vset.begin(); nsit != vset.end(); nsit++){*
* //...*
* MPI::Intracomm mcComm = multicastGroupMap[nsid]; *
* mcComm.Barrier();*
* if (mcComm == MPI::COMM_NULL){*
* continue;*
* }*
* if (rank == activeId){*
* //...*
* }else{*
* //...*
* }*
* mcComm.Barrier();*
*}*
replacing the barriers on workerComm i.e. the barriers that affect all
workers with barriers on mcComm within the inner loop. For some reason this
led to worse performance. Specifically, it increased the Shuffle time but
increased the rate (???) (will explain at the next point how the rate is
computed). Then I tried removing all barriers and the situation is even
worse (both the time increased and the rate decreased). It seems that the
barriers on workerComm help a lot even though they seem useless.
b) For the Allgatherv() case first I tried this:
*//Get all shuffling groups of the scheme *
*vector< NodeSet >& allSGs = cg->getShufflingGroups(); *
*//...*
*workerComm.Barrier();*
*//For each group, execute all the shuffling...*
*for (auto nsit = allSGs.begin(); nsit != allSGs.end(); nsit++){*
* //...*
* MPI::Intracomm mcComm = multicastGroupMap[nsid];*
* //If I am in the current group*
* if (mcComm != MPI::COMM_NULL){*
* //...*
* } *
*}*
*workerComm.Barrier();*
Then, I tried this:
*vector< NodeSet >& allSGs = cg->getShufflingGroups(); *
*//...*
*for (auto nsit = allSGs.begin(); nsit != allSGs.end(); nsit++){*
* //...*
* MPI::Intracomm mcComm = multicastGroupMap[nsid];*
* if (mcComm != MPI::COMM_NULL){*
* mcComm**.Barrier();*
* //...*
* mcComm**.Barrier();*
* } *
*}*
Finally, I tried this
*vector< NodeSet >& allSGs = cg->getShufflingGroups(); *
*//...*
*for (auto nsit = allSGs.begin(); nsit != allSGs.end(); nsit++){*
*workerComm.Barrier();*
* //...*
* MPI::Intracomm mcComm = multicastGroupMap[nsid];*
* //If I am in the current group*
* if (mcComm != MPI::COMM_NULL){*
* //...*
* } *
*workerComm.Barrier();*
*}*
All of these three methods seem to perform like the Bcast() case, maybe a
bit faster. I cannot be sure about this since as I will explain in next
point I am doing some mistake when measuring time so I measured manually
with my phone!
4. This is my main issue: I have implemented the Allgatherv() idea and it
works but I am trying to measure the time of the Shuffling phase as well as
the transmission rate. The way I am currently doing this can be seen at the
updated shuffle_before.cc I have attached. This is correct since I have set
the rate limit to 100Mbps with the Linux "tc" command and I have seen that
the rate computed is actually 100Mbps (of course, for small values of
[q^(k-1)]*(q-1)
i.e. for small number of required Broadcasts otherwise it is lower than 100
which is essentially what I am trying to improve).
Now the transmission time and/or the rate I compute for Allgatherv() is
wrong since even though I can see that it takes pretty much the same time
as in the case of Bcast() it prints completely unrealistic numbers as the
Shuffling total time (very high) and rate (very low). I have attached a
second file which includes the main parts of the code with the Allgatherv()
idea. The point is that each slave initializes some total time counter
"time", some transmission time counter "txTime" and some totalsize counter
in bytes "tolSize" to zero and then iterates through all groups that it
belongs to and adds the total time and the transmission time it took for
the send-receive function to complete (the only difference is that I
subtract the deserialization time from both counters since I don't want
this counted in order to have a valid comparison with the previous
implementation). It also adds the total size of data and metadata it
transmitted to the group. When all slaves are done they return the total
Shuffle time and the rate in Megabits/sec to the Master. The Master (code
omitted) just computes the average of these values (time and rate) and
prints them on the terminal. I am pretty sure that I miss something here
and I get wrong measurements.
Thanks for your time:)
On Tue, Nov 7, 2017 at 10:37 PM, George Bosilca <bosi...@icl.utk.edu> wrote:
> On Tue, Nov 7, 2017 at 6:09 PM, Konstantinos Konstantinidis <
> kostas1...@gmail.com> wrote:
>
>> OK, I will try to explain a few more things about the shuffling and I
>> have attached only specific excerpts of the code to avoid confusion. I have
>> added many comments.
>>
>> First, let me note that this project is an implementation of the Terasort
>> benchmark with a master node which assigns jobs to the slaves and
>> communicates with them after each phase to get measurements.
>>
>> The file shuffle_before.cc shows how I am doing the shuffling up to now
>> and the shuffle_after.cc the progress I made so far switching to
>> Allgatherv().
>>
>> I have also included the code that measures time and data size since it's
>> crucial for me to check if I have rate speedup.
>>
>> Some questions I have are:
>> 1. At shuffle_after.cc:61 why do we reserve *comm.Get_size() *entries for*
>> recv_counts* and not *comm.Get_size()-1 *? For example if I am rank k
>> what is the point of *recv_counts[k-1]*? I guess that rank k also
>> receives data from himself but we can ignore it, right?
>>
>
> No, you cant simply ignore it ;) allgather copies the same amount of data
> to all processes in the communicator ... including itself. If you want to
> argue about this reach out to the MPI standardization body ;)
>
>
>>
>> 2. My next concern is about the structure of the buffer *recv_buf[]*.
>> The documentation says that the data is stored there ordered. So I assume
>> that it's stored as segments of char* ordered by rank and the way to
>> distinguish them is to chop the whole data based on *recv_counts[]*. So
>> let G = {g1, g2, ..., gN} a group that exchanges data. Let's take slave g2:
>> Then segment *recv_buf[0 until **recv_counts[0]-1**] *is what g2
>> received from g1, *recv_buf[**recv_counts[0] until **recv_counts[1]-1**]
>> *is what g2 received from himself (ignore it), and so on... Is this idea
>> correct?
>>
>
> I don't know what documentation says "ordered", there is no such wording
> in the MPI standard. By carefully playing with the receive datatype I can
> do anything I want, including interleaving data from the different peers.
> But this is not what you are trying to do here.
>
> The placement in memory you describe is true if you use the displacement
> array as crafted in my example. The entry i in the displacement array
> specifies the displacement (relative to recvbuf) at which to place the
> incoming data from process i, so where you receive data has nothing to do
> with the amount you receive but with what you have in the displacement
> array.
>
>
>>
>> So I have written a sketch of the code at shuffle_after.cc which I also
>> try to explain how the master will compute rate, but at least I want to
>> make it work.
>>
>
> This code looks OK to me. I would however:
>
> 1. Remove the barriers on the workerComm. If the order of the
> communicators in the multicastGroupMap is identical on all processes
> (including communicators where they do not belong to) then the barriers are
> superfluous. However, if you try to protect your processes from starting
> the allgather collective to early, then you can replace the barrier
> on workerComm with one on mcComm.
>
> 2. The check "ns.find(rank) != ns.end()" should be equivalent to "mcComm
> == MPI_COMM_NULL" if I understand your code correctly.
>
> 3. This is an optimization. Remove all time exchanges outside the main
> loop. Instead of sending them one-by-one, keep them in an array and send
> the entire array once per CodedWorker::execShuffle, possible via an
> MPI_Allgatherv toward the master process in MPI_COMM_WORLD (in this case
> you can convert the "long long" into a double to facilitate the collective).
>
> George.
>
>
>
>>
>> I know that this discussion is getting long but if you have some free
>> time can you take a look at it?
>>
>> Thanks,
>> Kostas
>>
>>
>> On Tue, Nov 7, 2017 at 9:34 AM, George Bosilca <bosi...@icl.utk.edu>
>> wrote:
>>
>>> If each process send a different amount of data, then the operation
>>> should be an allgatherv. This also requires that you know the amount each
>>> process will send, so you will need a allgather. Schematically the code
>>> should look like the following:
>>>
>>> long bytes_send_count = endata.size * sizeof(long); // compute the
>>> amount of data sent by this process
>>> long* recv_counts = (long*)malloc(comm_size * sizeof(long)); //
>>> allocate buffer to receive the amounts from all peers
>>> int displs = (int*)malloc(comm_size * sizeof(int)); // allocate buffer
>>> to compute the displacements for each peer
>>> MPI_Allgather( &bytes_send_count, 1, MPI_LONG, recv_counts, 1, MPI_LONG,
>>> comm); // exchange the amount of sent data
>>> long total = 0; // we need a total amount of data to be received
>>> for( int i = 0; i < comm_size; i++) {
>>> displs[i] = total; // update the displacements
>>> total += recv_counts[i]; // and the total count
>>> }
>>> char* recv_buf = (char*)malloc(total * sizeof(char)); // prepare buffer
>>> for the allgatherv
>>> MPI_Allgatherv( &(endata.data), endata.size*sizeof(char),
>>> MPI_UNSIGNED_CHAR, recv_buf, recv_counts, displs, MPI_UNSIGNED_CHAR, comm);
>>>
>>> George.
>>>
>>>
>>>
>>> On Tue, Nov 7, 2017 at 4:23 AM, Konstantinos Konstantinidis <
>>> kostas1...@gmail.com> wrote:
>>>
>>>> OK, I started implementing the above Allgather() idea without success
>>>> (segmentation fault). So I will post the problematic lines hare:
>>>>
>>>> * comm.Allgather(&(endata.size), 1, MPI::UNSIGNED_LONG_LONG,
>>>> &(endata_rcv.size), 1, MPI::UNSIGNED_LONG_LONG);*
>>>> * endata_rcv.data = new unsigned char[endata_rcv.size*lineSize];*
>>>> * comm.Allgather(&(endata.data), endata.size*lineSize,
>>>> MPI::UNSIGNED_CHAR, &(endata_rcv.data), endata_rcv.size*lineSize,
>>>> MPI::UNSIGNED_CHAR);*
>>>> * delete [] endata.data;*
>>>>
>>>> The idea (as it was also for the broadcasts) is first to transmit the
>>>> data size as an unsigned long long integer, so that the receivers will
>>>> reserve the required memory for the actual data to be transmitted after
>>>> that. To my understanding, the problem is that each broadcasted data, let
>>>> D(s,G), as I explained in the previous email is not only different but also
>>>> has different size (in general). That's because if I replace the 3rd line
>>>> with
>>>>
>>>> * comm.Allgather(&(endata.data), 1, MPI::UNSIGNED_CHAR,
>>>> &(endata_rcv.data), 1, MPI::UNSIGNED_CHAR);*
>>>>
>>>> seems to work without seg. fault but this is pointless for me since I
>>>> don't want only 1 char to be transmitted. So if we see the previous image I
>>>> posted, imagine that the red, green and blue squares are different in size?
>>>> Can Allgather() even work then? If no, do you suggest anything else or I am
>>>> trapped in using the MPI_Bcast() as shown in Option 1?
>>>>
>>>> On Mon, Nov 6, 2017 at 8:58 AM, George Bosilca <bosi...@icl.utk.edu>
>>>> wrote:
>>>>
>>>>> On Sun, Nov 5, 2017 at 10:23 PM, Konstantinos Konstantinidis <
>>>>> kostas1...@gmail.com> wrote:
>>>>>
>>>>>> Hi George,
>>>>>>
>>>>>> First, let me note that the cost of q^(k-1)]*(q-1) communicators was
>>>>>> fine for the values of parameters q,k I am working with. Also, the whole
>>>>>> point of speeding up the shuffling phase is trying to reduce this number
>>>>>> even more (compared to already known implementations) which is a major
>>>>>> concern of my project. But thanks for pointing that out. Btw, do you know
>>>>>> what is the maximum such number in MPI?
>>>>>>
>>>>>
>>>>> Last time I run into such troubles these limits were: 2k for MVAPICH,
>>>>> 16k for MPICH and 2^30-1 for OMPI (all positive signed 23 bits integers).
>>>>> It might have changed meanwhile.
>>>>>
>>>>>
>>>>>> Now to the main part of the question, let me clarify that I have 1
>>>>>> process per machine. I don't know if this is important here but my way of
>>>>>> thinking is that we have a big text file and each process will have to
>>>>>> work
>>>>>> on some chunks of it (like chapters of a book). But each process resides
>>>>>> in
>>>>>> an machine with some RAM which is able to handle a specific amount of
>>>>>> work
>>>>>> so if you generate many processes per machine you must have fewer book
>>>>>> chapters per process than before. Thus, I wanted to avoid thinking in the
>>>>>> process-level rather than machine-level with the RAM limitations.
>>>>>>
>>>>>> Now to the actual shuffling, here is what I am currently doing
>>>>>> (Option 1):
>>>>>>
>>>>>> Let's denote the data that slave s has to send to the slaves in group
>>>>>> G as D(s,G).
>>>>>>
>>>>>> *for each slave s in 1,2,...,K{*
>>>>>>
>>>>>> * for each group G that s participates into{*
>>>>>>
>>>>>> * if (my rank is s){*
>>>>>> * MPI_Bcast(send data D(s,G))*
>>>>>> * }else if(my rank is in group G)*
>>>>>> * MPI_Bcast(get data D(s,G))*
>>>>>> * }else{*
>>>>>> * Do nothing*
>>>>>> * }*
>>>>>>
>>>>>> * }*
>>>>>>
>>>>>> *MPI::COMM_WORLD.Barrier();*
>>>>>>
>>>>>> *}*
>>>>>>
>>>>>> What I suggested before to speedup things (Option 2) is:
>>>>>>
>>>>>> *for each set {G(1),G(2),...,G(q-1)} of q-1 disjoint groups{ *
>>>>>>
>>>>>> * for each slave s in G(1)*
>>>>>> * if (my rank is s){*
>>>>>> * MPI_Bcast(send data D(s,G(1)))*
>>>>>> * }else if(**my rank is in** group G(1))*
>>>>>> * MPI_Bcast(get data D(s,G(1)))*
>>>>>> * }else{*
>>>>>> * Do nothing*
>>>>>> * }*
>>>>>> * }*
>>>>>>
>>>>>> * for each slave s in G(2)*
>>>>>> * if (my rank is s){*
>>>>>> * MPI_Bcast(send data D(s,G(2)))*
>>>>>> * }else if(**my rank is in** G(2))*
>>>>>> * MPI_Bcast(get data D(s,G(2)))*
>>>>>> * }else{*
>>>>>> * Do nothing*
>>>>>> * }*
>>>>>> * }*
>>>>>>
>>>>>> * ...*
>>>>>>
>>>>>> *for each slave s in G(q-1)*
>>>>>> * if (my rank is s){*
>>>>>> * MPI_Bcast(send data D(s,G(q-1)))*
>>>>>> * }else if(**my rank is in** G(q-1))*
>>>>>> * MPI_Bcast(get data D(s,G(q-1)))*
>>>>>> * }else{*
>>>>>> * Do nothing*
>>>>>> * }*
>>>>>> * }*
>>>>>>
>>>>>> * MPI::COMM_WORLD.Barrier();*
>>>>>>
>>>>>> *}*
>>>>>>
>>>>>> My hope was that I could implement Option 2 (in some way without
>>>>>> copying and pasting the same code q-1 times every time I change q) and
>>>>>> that
>>>>>> this could bring a speedup of q-1 compared to Option 1 by having these
>>>>>> groups communicate in parallel. Right, now I am trying to find a way to
>>>>>> identify these sets of groups based on my implementation, which involves
>>>>>> some abstract algebra but for now let's assume that I can find them in an
>>>>>> efficient manner.
>>>>>>
>>>>>> Let me emphasize that each broadcast sends different actual data.
>>>>>> There are no two broadcasts that send the same D(s,G).
>>>>>>
>>>>>> Finally, let's go to MPI_Allgather(): I am really confused since I
>>>>>> have never used this call but I have this image in my mind:
>>>>>>
>>>>>>
>>>>>>
>>>>> If every member of a group does a bcast to all other members of the
>>>>> same group, then this operation is better realized by an allgather. The
>>>>> picture you attached clearly expose the data movement pattern where each
>>>>> color box gets distributed to all members of the same communicator. You
>>>>> could also see this operation as a loop of bcast where the iterator goes
>>>>> over all members of the communicator and use it as a root.
>>>>>
>>>>>
>>>>>>
>>>>>> I am not sure what you meant but now I am thinking of this (let commG
>>>>>> be the intra-communicator of group G):
>>>>>>
>>>>>> *for each possible group G{*
>>>>>>
>>>>>> *if (my rank is in G){*
>>>>>> * commG.MPI_AllGather(**send data D(rank,G)**)*
>>>>>> * }**else{*
>>>>>> * Do nothing*
>>>>>> * }*
>>>>>>
>>>>>> *MPI::COMM_WORLD.Barrier();*
>>>>>>
>>>>>> *}*
>>>>>>
>>>>>
>>>>> This is indeed what I was thinking about, with the condition that you
>>>>> make sure the list of communicators in G is ordered in the same way on all
>>>>> processes.
>>>>>
>>>>> That being said, this communication pattern 1) generated a large
>>>>> barrier in your code; 2) as all processes will potentially be involved in
>>>>> many collective communications you will be hammering the network in a
>>>>> significant way (so you will have to take into account the network
>>>>> congestion); and 3) all processes need to have all memory for receive
>>>>> allocated for the buffers. Thus, even be implementing a nice communication
>>>>> scheme you might encounter some performance issues.
>>>>>
>>>>> Another way to do this is to instead of conglomerating all
>>>>> communications in a single temporal location you spread them out across
>>>>> time by imposing your own communication logic. This basically translate a
>>>>> set of blocking collective (bcast is a perfect target) into a pipelined
>>>>> mix. Instead of describing such a scheme here I suggest you read the
>>>>> algorithmic description of the SUMMA and/or PUMMA distributed matrix
>>>>> multiplication.
>>>>>
>>>>> George.
>>>>>
>>>>>
>>>>> I am not sure whether this makes sense since I am confused about the
>>>>>> correspodence of the data transmitted with Allgather() compared to the
>>>>>> notation D(s,G) I am currently using.
>>>>>>
>>>>>> Thanks.
>>>>>>
>>>>>>
>>>>>> On Tue, Oct 31, 2017 at 11:11 PM, George Bosilca <bosi...@icl.utk.edu
>>>>>> > wrote:
>>>>>>
>>>>>>> It really depends what are you trying to achieve. If the question is
>>>>>>> rhetorical: "can I write a code that does in parallel broadcasts on
>>>>>>> independent groups of processes ?" then the answer is yes, this is
>>>>>>> certainly possible. If however you add a hint of practicality in your
>>>>>>> question "can I write an efficient parallel broadcast between
>>>>>>> independent
>>>>>>> groups of processes?" then I'm afraid the answer will be a negative one.
>>>>>>>
>>>>>>> Let's not look at how you can write the multiple bcast code as the
>>>>>>> answer in the stackoverflow is correct, but instead look at what
>>>>>>> resources
>>>>>>> these collective operations are using. In general you can assume that
>>>>>>> nodes
>>>>>>> are connected by a network, able to move data at a rate B in both
>>>>>>> directions (full duplex). Assuming the implementation of the bcast
>>>>>>> algorithm is not entirely moronic, the bcast can saturate the network
>>>>>>> with
>>>>>>> a single process per node. Now, if you have multiple processes per node
>>>>>>> (P)
>>>>>>> then either you schedule them sequentially (so that each one has the
>>>>>>> full
>>>>>>> bandwidth B) or you let them progress in parallel in which case each
>>>>>>> participating process can claim a lower bandwidth B/P (as it is shared
>>>>>>> between all processes on the nore).
>>>>>>>
>>>>>>> So even if you are able to expose enough parallelism, physical
>>>>>>> resources will impose the real hard limit.
>>>>>>>
>>>>>>> That being said I have the impression you are trying to implement an
>>>>>>> MPI_Allgather(v) using a series of MPI_Bcast. Is that true ?
>>>>>>>
>>>>>>> George.
>>>>>>>
>>>>>>> PS: Few other constraints: the cost of creating the q^(k-1)]*(q-1)
>>>>>>> communicator might be prohibitive; the MPI library might support a
>>>>>>> limited
>>>>>>> number of communicators.
>>>>>>>
>>>>>>>
>>>>>>> On Tue, Oct 31, 2017 at 11:42 PM, Konstantinos Konstantinidis <
>>>>>>> kostas1...@gmail.com> wrote:
>>>>>>>
>>>>>>>> Assume that we have K=q*k nodes (slaves) where q,k are positive
>>>>>>>> integers >= 2.
>>>>>>>>
>>>>>>>> Based on the scheme that I am currently using I create
>>>>>>>> [q^(k-1)]*(q-1) groups (along with their communicators). Each group
>>>>>>>> consists of k nodes and within each group exactly k broadcasts take
>>>>>>>> place
>>>>>>>> (each node broadcasts something to the rest of them). So in total
>>>>>>>> [q^(k-1)]*(q-1)*k MPI broadcasts take place. Let me skip the details
>>>>>>>> of the
>>>>>>>> above scheme.
>>>>>>>>
>>>>>>>> Now theoretically I figured out that there are q-1 groups that can
>>>>>>>> communicate in parallel at the same time i.e. groups that have no
>>>>>>>> common
>>>>>>>> nodes and I would like to utilize that to speedup the shuffling. I have
>>>>>>>> seen here https://stackoverflow.com/questions/11372012/mpi-severa
>>>>>>>> l-broadcast-at-the-same-time that this is possible in MPI.
>>>>>>>>
>>>>>>>> In my case it's more complicated since q,k are parameters of the
>>>>>>>> problem and change between different experiments. If I get the idea
>>>>>>>> about
>>>>>>>> the 2nd method that is proposed there and assume that we have only 3
>>>>>>>> groups
>>>>>>>> within which some communication takes places one can simply do:
>>>>>>>>
>>>>>>>> *if my rank belongs to group 1{*
>>>>>>>> * comm1.Bcast(..., ..., ..., rootId);*
>>>>>>>> *}else if my rank belongs to group 2{*
>>>>>>>> * comm2.Bcast(..., ..., ..., rootId);*
>>>>>>>> *}else if my rank belongs to group3{*
>>>>>>>> * comm3.Bcast(..., ..., ..., rootId);*
>>>>>>>> *} *
>>>>>>>>
>>>>>>>> where comm1, comm2, comm3 are the corresponding sub-communicators
>>>>>>>> that contain only the members of each group.
>>>>>>>>
>>>>>>>> But how can I generalize the above idea to arbitrary number of
>>>>>>>> groups or perhaps do something else?
>>>>>>>>
>>>>>>>> The code is in C++ and the MPI installed is described in the
>>>>>>>> attached file.
>>>>>>>>
>>>>>>>> Regards,
>>>>>>>> Kostas
>>>>>>>>
>>>>>>>>
>>>>>>>> _______________________________________________
>>>>>>>> users mailing list
>>>>>>>> users@lists.open-mpi.org
>>>>>>>> https://lists.open-mpi.org/mailman/listinfo/users
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>
>
shuffle_before.cc
Description: Binary data
shuffle_after.cc
Description: Binary data
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