How to use the MPI-3 Shared Memory in Intel Xeon Phi Processors

Published Date
01 - May - 2017
| Last Updated
01 - May - 2017
 
How to use the MPI-3 Shared Memory in Intel Xeon Phi Processors

This whitepaper introduces the MPI-3 shared memory feature, the corresponding APIs, and a sample program to illustrate the use of MPI-3 shared memory in the Intel® Xeon Phi™ processor.

Introduction to MPI-3 Shared Memory

MPI-3 shared memory is a feature introduced in version 3.0 of the message passing interface (MPI) standard. It is implemented in Intel® MPI Library version 5.0.2 and beyond. MPI-3 shared memory allows multiple MPI processes to allocate and have access to the shared memory in a compute node. For applications that require multiple MPI processes to exchange huge local data, this feature reduces the memory footprint and can improve performance significantly.

In the MPI standard, each MPI process has its own address space. With MPI-3 shared memory, each MPI process exposes its own memory to other processes. The following figure illustrates the concept of shared memory: Each MPI process allocates and maintains its own local memory, and exposes a portion of its memory to the shared memory region. All processes then can have access to the shared memory region. Using the shared memory feature, users can reduce the data exchange among the processes.

By default, the memory created by an MPI process is private. It is best to use MPI-3 shared memory when only memory needs to be shared and all other resources remain private. As each process has access to the shared memory region, users need to pay attention to process synchronization when using shared memory.

Sample Code

In this section, sample code is provided to illustrate the use of MPI-3 shared memory.

A total of eight MPI processes are created on the node. Each process maintains a long array of 32 million elements. For each element j in the array, the process updates this element value based on its current value and the values of the element j in the corresponding arrays of two nearest processes, and the same procedure is applied for the whole array. The following pseudo-code shows when running the program for eight MPI processes with 64 iterations:

Repeat the following procedure 64 times:

for each MPI process n from 0 to 7:
    for each element j in the array A[k]:
        An[j] ← 0.5*An[j]  + 0.25*Aprevious[j] + 0.25*Anext[j]

where An is the long array belonging to the process n, and An [j] is the value of the element j in the array belonging to the process n. In this program, since each process exposes it to local memory, all processes can have access to all arrays, although each process just needs the two neighbor arrays (for example, process 0 needs data from processes 1 and 7, process 1 needs data from processes 0 and 2,…).

Besides the basic APIs used for MPI programming, the following MPI-3 shared memory APIs are introduced in this example:

  • MPI_Comm_split_type: Used to create a new communicator where all processes share a common property. In this case, we pass MPI_COMM_TYPE_SHARED as an argument in order to create a shared memory from a parent communicator such as MPI_COMM_WORLD, and decompose the communicator into a shared memory communicator shmcomm.
  • MPI_Win_allocate_shared: Used to create a shared memory that is accessible by all processes in the shared memory communicator. Each process exposes its local memory to all other processes, and the size of the local memory allocated by each process can be different. By default, the total shared memory is allocated contiguously. The user can pass an info hint “alloc_shared_noncontig” to specify that the shared memory does not have to be contiguous, which can cause performance improvement, depending on the underlying hardware architecture. 
  • MPI_Win_free: Used to release the memory.
  • MPI_Win_shared_query: Used to query the address of the shared memory of an MPI process.
  • MPI_Win_lock_all and MPI_Win_unlock_all: Used to start an access epoch to all processes in the window. Only shared epochs are needed. The calling process can access the shared memory on all processes.
  • MPI_Win_sync: Used to ensure the completion of copying the local memory to the shared memory.
  • MPI_Barrier: Used to block the caller process on the node until all processes reach a barrier. The barrier synchronization API works across all processes.

Basic Performance Tuning for Intel® Xeon Phi™ Processor

This test is run on an Intel Xeon Phi processor 7250 at 1.40 GHz with 68 cores, installed with Red Hat Enterprise Linux* 7.2 and Intel® Xeon Phi™ Processor Software 1.5.1, and Intel® Parallel Studio 2017 update 2. By default, the Intel compiler will try to vectorize the code, and each MPI process has a single thread of execution. OpenMP* pragma is added at loop level for later use. To compile the code, run the following command line to generate the binary mpishared.out:

$ mpiicc mpishared.c -qopenmp -o mpishared.out
$ mpirun -n 8 ./mpishared.out

Elapsed time in msec: 5699 (after 64 iterations)

To explore the thread parallelism, run four threads per core, and re-compile with –xMIC-AVX512 to take advantage of Intel® Advanced Vector Extensions 512 (Intel® AVX-512) instructions:

$ mpiicc mpishared.c -qopenmp -xMIC-AVX512 -o mpishared.out
$ export OMP_NUM_THREADS=4
$ mpirun -n 8 ./mpishared.out
Elapsed time in msec: 4535 (after 64 iterations)

As MCDRAM in this system is currently configured as flat, the Intel Xeon Phi processor appears as two NUMA nodes. The node 0 contains all CPUs and the on-platform memory DDR4, while node 1 has the on-packet memory MCDRAM:

$ numactl -H
available: 2 nodes (0-1)
node 0 cpus: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271

node 0 size: 98200 MB
node 0 free: 92775 MB
node 1 cpus:
node 1 size: 16384 MB
node 1 free: 15925 MB
node distances:
node   0   1
  0:  10  31
  1:  31  10

To allocate the memory in the MCDRAM (node 1), pass the argument –m 1 to the command numactl as follows:

$ numactl -m 1 mpirun -n 8 ./mpishared.out
Elapsed time in msec: 3070 (after 64 iterations)

This simple optimization technique greatly improves performance speeds.

Summary

This whitepaper introduced the MPI-3 shared memory feature, followed by sample code, which used MPI-3 shared memory APIs. The pseudo-code explained what the program is doing along with an explanation of shared memory APIs. The program ran on an Intel Xeon Phi processor, and it was further optimized with simple techniques.

For more such intel Modern Code and tools from Intel, please visit the Intel® Modern Code

Source:https://software.intel.com/en-us/using-mpi-3-shared-memory-in-xeon-phi-processors