Multi GPU Performance of Conjugate Gradient Algorithm with Staggered Fermions

Multi GPU Performance of Conjugate Gradient Algorithm with Staggered Fermions

Abstract

We report results of the performance test of GPUs obtained using the conjugate gradient (CG) algorithm for staggered fermions on the MILC fine lattice (). We use GPUs of nVIDIA GTX 295 model for the test. When we turn off the MPI communication and use only a single GPU, the performance is 35 giga flops in double precision, which corresponds to 47% of the peak. When we turn on the MPI communication and use multi-GPUs, the performance is reduced down to 12.3 giga flops. The data transfer through the infiniband network and PCI-E bus I/O is a main bottle neck. We suggest two potential solutions of how to optimize the data transfer.

\ShortTitle

Multi GPU Performance \FullConference The XXVIII International Symposium on Lattice Field Theory, Lattice2010
June 14-19, 2010
Villasimius, Italy

1 Introduction

There has been a significant progress in accelerating the computational speed. CPU has kept improving its computing power but does not yet quench the thirst of those demanding users who need more computing power for their numerical challenges such as lattice QCD. One bailout was to make a CPU cluster to do the parallel processing, which also causes a problem of how to handle the scalable communication between nodes. Lattice group at Columbia University developed customized machines for lattice QCD such as QCDOC [1], which has a great performance in the nearest neighbor communication.

At present, graphic processing units (GPU) opens a new era for high performance computing. GPU is originally designed to handle 3-dimensional graphic images. GPU is composed of many tiny multi processors which are more appropriate architecture to handle the single instruction multiple data (SIMD) than multi cores of CPU. In addition, the evolution of GPU is very fast. In Table 1, we summarize the peak performance of the current highend GPUs in the market [2]. The enormous computing power of GPUs is highly beneficial to lattice QCD.

model peak speed (SP) peak speed (DP) memory bandwidth (GB/s)
GTX 285 1062.72 GF 88.6 GF 159.0
GTX 295 1788.48 GF 149 GF 223.8
GTX 480 1344.96 GF 168 GF 177.4
C2050 1.03 TF 515 GF 144.0
Table 1: GPU models and their peak performance. Here, SP (DP) means single (double) precision. GF (TF) means giga (tera) flops. GB/s means giga bytes per second.

There are several ways to implement the GPU code in the market: Nvidia CUDA API, Open Graphic Library (Open GL),and Open Computing Language (Open CL). In this paper, we focus on CUDA and its applications. The CUDA provides us a user-friendly programming environment based on the C, C++ programming language for GPU. All of our code are compiled and tested in CUDA version 2.3 and compute capability 1.3 mode. We make the CUDA version of CG subroutine that is implemented as a part of the Columbia Physics System (CPS) library.

Let us turn to the hardware. We have constructed a GPU cluster whose name is David. The David cluster has 32 nodes which are connected through the 20 giga bit infiniband network. Each node has a Intel core i7 920 Processor and two of nVIDIA GTX 295 graphic cards1. We use LINUX Cent OS version 5.5 as an operating system of this cluster.

2 CG Implementation using CUDA

Conjugate gradient (CG) algorithm [6] is an iterative method for solving a linear algebraic equation of the following form.

(1)

where is a positive definite Hermitian matrix. and are complex vectors in the n dimensional space. Matrix and vector are given and vector is a solution that we want to obtain. Using the CG method we can get the solution up to the numerical precision that we want to achieve.





for
  
  
  
  
     
  

Figure 1: Conjugate gradient algorithm

In Fig. 1, we show the structure of CG algorithm. In the CG sequence, we have a number of linear algebra equations such as vector addition, dot product, and scalar multiplication and so on. All of these linear operations are implemented using CUBLAS library [3], except for the Dirac operation. Note that these operations are not dominant in CG operation, and so CUBLAS Library is good enough to handle them.

In Fig. 1, and are Dirac operations with staggered fermions [7]. Basically, the Dirac operation is a matrix-vector multiplication. This is dominant in CG. The matrix is defined as follows.

(2)
(3)
(4)
(5)

Here, note that the phase factor is already multiplied to the gauge link in the preconditioning part. is a given source vector and is a staggered fermion field which corresponds to the solution.

At a single site on the lattice, a single Dirac operation of takes 1584 bytes of data transfer and 576 floating point calculations. Let us consider a MILC fine lattice of . A single Dirac operation over the entire even sites of the lattice takes 0.6 billion floating point calculations and 1.6 giga bytes of data transfer. When we use GTX 295 GPUs, it is easy to find out that the bottle neck is in the data transfer.

3 CUDA CG code Optimization

Starting CUDA programming itself is easy and straight-forward. We change 4 dimensional for-loops into the CUDA thread index. The CUDA makes each core of the GPU perform the CG calculation at multiple lattice sites in parallel. This simple modification is easy but does not provide a good performance. It runs at about 1 giga flops per GPU (GTX 295). This is only twice faster than CPU. Hence, we must improve the performance of the CG code, which we will explain in the following subsections in detail.

3.1 Coalesced Memory Access

Figure 2: Data structure for coalesced memory access. CPU read gauge link data in serial way (upper part). But in GPU, multiple cores can read different data simultaneously. For maximum GPU memory bandwidth, the data addresses for simultaneous reading and writing should be continuous and multiples of 16 (lower part).

Fig. 2 shows the difference in the memory access pattern between CPU and GPU. The CPU code accesses each memory address in sequential way. In GPU, multiple cores access different memory addresses in parallel. If we do not align the memory address in a correct way, then we can not get the maximum bandwidth of data transfer in GPU. The correct way is called a coalesced memory access. In the coalesced memory access, the data should be grouped into a multiple of 16 as one can see in Fig. 2. Then the data can be accessed simultaneously from multiple cores at the highest speed [2]. We have changed our gauge link data and fermion vectors into the coalesced access pattern. As a result, we gain a 8.5 times performance enhancement.

3.2 Register memory

The GPUs of Nvidia have various kinds of memory area: constant memory, registers, shared memory, and so on. Registers and shared memory are fast on-chip memory. Hence, if we use them properly, we can reduce time for global memory access. As a result, we can enhance the overall performance of GPU. In the Compute Capability 1.32, each GPU (GTX 295) has 16k bytes of shared memory and 64k bytes of registers. However, when we use double precision in floating point calculations, shared memory has intrinsic memory bank-conflict problem3. The bank-conflict problem means that the data transfer between GPU and shared memory becomes serialized and slows down by factor of 16 [2]. Hence, when we calculate in double precision, the register memory shows significantly better performance. Therefore if we use registers instead of shared memory, we can speed up the program by factor of 3. Hence, the performance reaches 25 giga flops.

3.3 SU(3) reconstruction

In the CG program, the bottle neck is in the data transfer between GPU to GPU memory. Hence, if we reduce the size of data transfer, then the performance can be improved in principle. Because gauge links are SU(3) matrices, we can use the 8 or 12 parameters for the SU(3) matrix reconstruction [5]. For example, the 3rd row of matrix elements can be reconstructed by using the following equation.

(6)

Vectors correspond 1st, 2nd ,3rd rows of a SU(3) matrix, respectively. This reduces the amount of data transfer by 1/3. On the other hand, we need to do additional 48 floating point calculations are needed so that the floating point calculation increases by 67%. Furthermore, this reconstruction uses more registers. As a result, this makes the CUDA occupancy4 decrease. The net gain is about 7%. Hence, the reconstruction method is not helpful.

3.4 Additional Tune-up

The CUDA occupancy is a good criterion to determine how efficiently the kernel runs on the GPU. It is not a simple object involved in many factors such as the number of registers in use, size of shared memory in use, number of active threads, and so on. Hence, it is so complicated that there is no cure-all solution in general.

(7)
(8)

We can reduce the number of registers in use per thread down below 64 by using Eq. 8. Usual matrix-vector multiplication is done by Eq. 7. In that way, each thread needs total 77 registers. But if we use Eq. 8, then, we need only 61 registers. As a result, 2 threads blocks can be launched for each multiprocessor. This give us a 7% gain.

Reducing the branch code5 also gives a better performance. GPU is not good in branch prediction. Hence, removing unwanted branch code can increase the performance. As a result, it provides additional 30% of performance enhancement to our code.

In addition, we use bit operator and loop unrolling. Each of them gives a few % of performance enhancements. After all the optimizations, the final performance is about 35 giga flops, which is 47% of the peak performance in GTX 295. The GPU code is 76 times faster than the CPU code at present.

4 Multi GPU usage

In our program, we use MPI for multi GPU implementation. First, each node collects fermion vectors in lattice boundary on GPU memory space. Next, collected data are downloaded to host memory. Then, data are transferred to neighbor nodes by infiniband network (MPI). Finally, data in the host memory is uploaded to GPU memory and repeat the CG calculation. Compared with single GPU case, using multi GPU needs network communication by infiniband and PCI-E bus I/O. Unfortunately, data transfer between GPU nodes makes the program slow down dramatically as one can see in Table 2.

4 node(ms) 8 node(ms)
GPU calculation time 4.7 2.45
boundary data collect 0.9 0.5
gpu memory to host memory 2.9 2.1
MPI communication 2.3 1.8
host memory to gpu memory 3.4 1.7
Total time 14.5 8.6
Table 2: Time stamps of CG program using multi GPUs. Here, ms denotes mili seconds.

In Table 2, we present elapsed time of each operation. Here, the data transfer is dominant ( 70%). The floating point calculation of GPU occupies only 32% in 4 node calculation and the rest is for data transfer. So we have to make the data transfer faster for better performance. For better MPI performance, we use the MPI asynchronous network communication mode so that the CPU can run the next jobs simultaneously during the MPI data transfer . Fortunately, CUDA also supports asynchronous data transfer between GPU memory and CPU memory. Hence, if we can overlap cudamemcpy with MPI communication by using asynchronous communication, then total communication time can be reduced by almost 1/3 in principle.

5 Future Perspectives

Unfortunately, the idea to overlap the network I/O with cudamemcpy I/O is not working in our machine. There are two problems. First, simultaneous bi-directional memcpy between GPU and CPU is not supported yet. Our GPU is GTX 295 which does not support bi-directional memory copy Therefore, GPU download sequence and upload sequence cannot be overlapped. The second problem is that the page locked memory cannot be shared by MPI and CUDA. If we use asynchronous memcpy in CUDA or MPI simultaneously, then each operation needs to use its own page locked memory. But these memory regions cannot be shared by them yet. The two regions of page locked memory should be located in different memory areas. Hence, it needs additional memory copy step. This makes the communication optimization more difficult.

But there are possibilities to solve this problem. Actually, the 1st problem is not a problem anymore. From the FERMI version of Tesla GPUs, they have 2 memory copy engines which support the bi-directional memory copy [4]. And 2nd problem can also be solved by using the GPU-direct technology of Mellanox [8]. GPU-direct technology enables us sharing the page locked memory between GPU and MPI. Hence, additional memory copy between two page locked areas is not necessary. Unfortunately, we have not tested this functions yet, because we don‘t have Mellanox device and nVIDIA Tesla GPU. We expect that our machine (Qlogic) can also support these functions soon.

In near future, mixed precision method will be also implemented for CG, which will enhance the performance significantly.

6 Conclusion

By using GPUs, we can get a good performance in the CG algorithm for staggered fermions. The final performance is about 35 giga flops per GPU (GTX 295). GPU is 75 times faster result then CPU. We notice that data transfer between GPU and GPU memory is a bottle-neck. For better performance, various optimization methods are used. Including the MPI network communication, the performance is reduced down to 12.3 giga flops in double precision. At present, we continue working for the better performance.

The CUDA code of CG with multi GPUs is running in the production mode to calculate hadron spectrum and weak matrix elements relevant to CP violation in the neutral kaon system [9, 10] at present.

Footnotes

  1. At present, GTX 295 graphic cards are upgraded to nVIDIA GTX 480.
  2. one of the runtime version in CUDA program
  3. From compute capability 2.0, there is no bank-conflict using double precision in shared memory
  4. The CUDA occupancy is the ratio of the number of active threads involved in the calculation to the maximum number of threads.
  5. A branch code means part of the code which uses if, else if, switch, and so on.

References

  1. P. A. Boyle, D. Chen, N. H. Christ, M. A. Clark, Et al, IBM J. RES & DEV. VOL 49, NO 2/3, MARCH/MAY 2005.
  2. Nvidia Corporation, “NVIDIA CUDA programming Guide”, 2010, http://developer.download.nvidia.com/compute/cuda/3_2/toolkit/docs/CUDA_C_Programming_Guide.pdf
  3. Nvidia Corporation, “NVIDIA CUBLAS User Guide”, 2010, http://developer.download.nvidia.com/compute/cuda/3_2/toolkit/docs/CUBLAS_Library.pdf
  4. Nvidia Corporation, “NVIDIA‘s Next Generation CUDA Computer Architecture: Fermi”, http://www.nvidia.com/content/PDF/fermi_white_papers/NVIDIA_Fermi_Compute_Architecture_Whitepaper.pdf
  5. M.A. Clark, R. Babich, K. Barros, R.C. Brower, C. Rebbi, “Solving lattice QCD systems of equations using mixed precision solvers on GPUs”, Computer Physics Communication, 182 (2010), 1517-1528.
  6. John K. Reid, On the Method of Conjugate Gradients for the Solution of Large Sparse Systems of Linear Equations, Large Sparse Sets of Linear Equations (London and New York) (John K. Reid, ed.), Academic Press, London and New York, 1971, pp. 231-254.
  7. Leonard Susskind, “Lattice fermions ”, Phys. Rev. D, 16, 3031-3039 (1977).
  8. Mellanox Technologies Corporation,“NVIDIA GPU Direct Technology-Accelerating GPU-based Systems”, http://www.mellanox.com/pdf/whitepapers/TB_GPU_Direct.pdf
  9. Taegil Bae, et al., [arXiv:1008.5179].
  10. Boram Yoon, et al., PoS (LATTICE 2010) 319; [arXiv:1010.4778].
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