# Background¶

EMGeo is composed of two geophysical imaging applications: one for subsurface imaging using electromagnetic data and another using seismic data. Although the applications model different physics (Maxwell's equations in one case, the elastic wave equation in another) they have much in common. We focus on the more involved part for solving the forward pass of the inverse scattering for the seismic part.

The code takes advantage of high-level data parallelism to solve many semi-independent sub-problems concurrently, yielding excellent scaling. Second, the runtime is dominated by the application of a linear solver, accounting for > 90% of wallclock time. The solver is dominated by memory bandwidth intensive operations like sparse matrix-vector multiply (SpMV), dot products, vector triads, etc. It uses double complex precision and represents the sparse matrix in ELLPACK format.

The equation which is solved by EMGeo is

\left[1-\langle \mathbf{b}\rangle \mathbf{D}_\tau \cdot (\langle \mathbf{k}_\mu\rangle \circ \mathbf{D}_v) \right]\mathbf{v}_q=\mathbf{f}_q

Here, $\mathbf{b}$ and $\mathbf{k}_\mu$ are parameters describing the medium, \mathbf{v} is a velocity vector for frequency q and \mathbf{f} the source vector in the frequency domain. The matrices $D_\tau$ and $D_v$ can be decomposed spatially and expressed in sparse format via

\mathbf{D}_\tau = \left(\begin{array}{cccccc} \tilde{D}_x & D_y & D_z & \tilde{D}_x & 0 & \tilde{D}_x\\ \tilde{D}_y & D_x & 0 & \tilde{D}_y & D_z & \tilde{D}_y\\ \tilde{D}_z & 0 & D_x & \tilde{D}_z & D_y & \tilde{D}_z\\ \end{array}\right)^T
\mathbf{D}_v = \left(\begin{array}{cccccc} D_x & \tilde{D}_y & \tilde{D}_z & 0 & 0 & 0\\ 0 & \tilde{D}_x & 0 & \tilde{D}_y & \tilde{D}_z & 0\\ 0 & 0 & \tilde{D}_x & 0 & \tilde{D}_y & D_z\\ \end{array}\right)

The components are derivative operators coupling only nearest neighbors in the indicated directions. Each single entry is describing an $N_d\times N_d$ sub matrix, where $d$ is the corresponding direction. Therefore, these two matrices are large but very sparse. The sparsity patterns are displayed in Fig. 1.

Fig.1. Sparsity patterns of $D_\tau$ and (transposed) $D_v$.

The main challenging aspect in this effort of optimizing EMGeo for KNL is to improve the performance of the SpMV product shown above, as it amounts to two thirds of the time spent in the linear solver. However, SpMV operation is notoriously memory bandwidth-bound as its naive arithmetic intensity is low. In the following, we will describe how we tackle this challenge.

## Approach¶

### Memory Optimizations¶

We apply techniques to reduce the memory traffic and increase the in-cache data reuse in the SpMV product of EMGeo code. We replace the ELLPack sparse matrix format, which is used in EMGeo code, with Sliced ELLPack (SELLPack) format to reduce unnecessary FLOPS and memory transfers. We also apply cache blocking techniques to increase the SpMV product operation Arithmetic Intensity (AI). Namely, we experimented with Spatial Blocking (SB) and multiple Right Hand Side (mRHS) cache blocking.

EMGeo uses ELLPack data format because the maximum number of Non-Zero (NNZ) elements in each row is 12. ELLPack allocates a rectangular matrix, setting the width to the largest row width and pads smaller rows with zeroes. Most of the rows in $D_\tau$ matrix contain 12 NNZ/row, so the padding overhead of the rows is minimal. However, half of the rows in $D_v$ matrix contain 8 NNZ/row, so we use the SELLPack format proposed in 1. SELLPack format allows defining different number of NNZ/row in different sections of the same matrix. We reorder $D_v$ matrix, as shown in Fig. 2, to have 12 NNZ/row in the first half of the matrix and 8 NNZ/row in the second half of the matrix. This effectively saves 17% of $D_v$ SpMV product operations.

We apply SB techniques 2, 3 to reduce the main memory data traffic of the multiplied vector. In the regular update order of the SpMV product, the elements of the multiplied vector are accessed several times. As the vectors are larger than the cache memory, the vector elements are brought from main memory several times. For example, the regular update order of $D_v$ SpMV product loads the multiplied vector 5 times from main memory (see below for more analysis details). SB changes the operation order in the matrix, such that matrix rows touching the same vector elements are updated together, while the vector elements are in the cache memory. This idea is illustrated in Fig. 2b. First the SpMV product of the red rows of the matrix is performed, while keeping the red part of the vector in the cache memory. Then the blue part is updated similarly, etc. As long as the block size ts in the cache memory, each element of the vector is brought once from the main memory. We show below that combining SB and mRHS blocking can be inefficient in KNL due to the small cache memory size. Therefore, we reorder the loop over the Matrix components (i.e., row blocks of size N) with the loop over the rows of one component, which effectively reduces the SB block size to one row. As a result, the first row of each matrix component is evaluated first, then the next row, etc..

EMGeo solves the Equation above for multiple independent sources (RHS). In the RHS cache blocking approach we perform the SpMV product over several RHS’s while a block of the matrix is in the cache memory, similar to 3. RHS blocking amortizes the cost of loading the matrix from main memory, which is the dominant contributor of the main memory data traffic. The RHS blocking update order, combined with SB, is illustrated in Fig. 2c. First, each red block of the matrix performs the SpMV product over all the RHS, while the block is in the cache memory, then the blue blocks are updated, etc..

Fig. 2: Illustration of plain matrix $D_v$ (panel a), with spatial blocking (SB, panel b) and multiple right-hand-side blocking (mRHS, panel c). The colored bands in the matrix denote elements which are involved in the matrix multiplication with the vector elements on the right-hand-side and stored into the corresponding elements of the same color on the left-hand-side.

## Performance Results¶

### $D_v$ Kernel¶

We use a benchmark code for the $D_v$ SpMV product in the EMGeo application, as the SpMV products consume significant portion of the code runtime. Fig. 3 shows the performance improvements and the transferred memory volumes improvements model prediction and measurement (based on actually transferred memory, measured with vTunes -collect memory-access option), using different optimization combinations.

Fig. 3: effect of various cache optimizations on the $D_v$ SpMV kernel for Haswell (top panel) and KNL (bottom panel).

We show results for a single socket Haswell and KNL processors, using a grid of size $110 \times 110 \times 105$. The results in the rightmost block in Fig. 3 use SB and loop reordering in Haswell and KNL, respectively, in addition to the SELL and mRHS optimizations. We do not use SB in KNL optimizations because it results in less performance than the naïve code. In the RHS blocking optimization, we use 32 and 64 RHS block size in Haswell and KNL, respectively. The SpMV operation is repeated 100 times, where every 10 repetitions are timed together. We report the median time of the experiments. KNL results are reported in SNC2-flat mode using MCDRAM only, as the data fits in the MCDRAM in the production code. We observe similar performance in all KNL modes. Using the MCDRAM memory, compared to using the DDR4, increases the performance in KNL by a factor of 3.0x and 4.2x in the naïve and optimized codes, respectively. KNL is faster than a single socket Haswell processor by over a factor of 3x, which is mainly attributed to the higher memory bandwidth.

The roofline analysis 4, 5, []^6] of the $D_v$ benchmark results is shown in Fig. 4, where we used the techniques described here using Intel Software Development Emulator 7 to prepare these results. The roofline model shows that our RHS blocking technique significantly improves the AI. The code is still memory bandwidth-bound, so it cannot benefit from vectorization optimizations.

Fig. 4: Roofline analysis comparing out best kernel variants for $D_v$ on Haswell (top panel) and KNL (bottom panel). The attached labels 4.8x and 5x denote the speedup over the original code on both architectures respectively.

### Full EMGeo Application¶

We measure the time in the major components of EMGeo code. The time is mainly dominated by the IDR solve, which is in turn is dominated by the SpMV. Our experiments consist of single Cori node and single KNL processor. We summarize the single node results in Fig. 5. The experiment evaluates 32 RHS with 500 Krylov solver iterations, using a $100 \times 50 \times 50$ grid size, which is comparable to the subdomain size in productions scenarios. The original code does not have shared memory parallelization, so it uses one MPI rank per core. The optimized code uses 32 RHS block size and uses single MPI rank per socket in Haswell experiment. We observe that the SpMV product takes over half of the runtime in the original IDR solver implementation. Our SpMV product optimizations result in 3.40x speedup in the application, which is less than $D_v$ SpMV product improvements in the benchmark. $D_\tau$ SpMV product has less benefit from our optimization because it does not utilize the SELLPack format. Moreover, the SpMV product kernels in the application are fused with other kernels to improve the data locality. The reduction in the MPI ranks by a factor of 16× has significant impact in speeding up the code, as less ranks are involved in the reductions and halo exchange operations.

Fig 5.: Impact of our optimizations on the performance of the full EMGeo application (forward pass). The various parts of the stacked bars denote the total time spent in SpMV (blue), communications (yellow) and linear algebra and other solver-related operations (green). The labels denote the overall speedup over the unoptimized kernel.

We further observe that results for KNL in SNC-2 flat mode is similar to the performance we obtain in the other modes. The whole application data fits in the MCDRAM memory, so we run the code using the MCDRAM only. The best performance in KNL is observed at two threads per core. We tuned the MPI ranks vs. the OpenMP threads manually in the optimized code. We observe 1.92× speedup in the code, where the SpMV and communication operations run about 2.3x and 3.1x faster, respectively. Using the MCDRAM memory, compared to using the DDR4 only, increases the performance in KNL by a factor of 2.8x and 3.1x in the naïve and optimized codes, respectively.

## Conclusions¶

We obtained performance improvements by reducing the data traffic to the main memory in the SpMV products of the EMGeo code. We used SB, SELLPack sparse matrix format, and most importantly the RHS cache blocking technique. We identified relevant optimizations and understand the optimization quality and issues. RHS blocking provides signi cant performance improvements and prepares the code to use block IDR algorithm and overlap computations with communication in the solver.

1. Monakov, A., Lokhmotov, A., Avetisyan, A.: High Performance Embedded Architectures and Compilers: 5th International Conference, HiPEAC 2010, Pisa, Italy, Jan- uary 25-27, 2010. Proceedings, chap. Automatically Tuning Sparse Matrix-Vector Multiplication for GPU Architectures, pp. 111–125. Springer Berlin Heidelberg, Berlin, Heidelberg (2010), https://dx.doi.org/10.1007/978-3-642-11515-8_10

2. Datta, K.: Auto-tuning Stencil Codes for Cache-Based Multicore Platforms. Ph.D. thesis, EECS Department, University of California, Berkeley (Dec 2009), http: //www.eecs.b erkeley.edu/Pubs/TechRpts/2009/EECS-2009-177.html

3. Haase, G., Reitzinger, S.: Cache issues of algebraic multigrid methods for linear systems with multiple right-hand sides. SIAM Journal on Scientific Computing 27(1), 1–18 (2005), https://dx.doi.org/10.1137/S1064827502405112

4. Williams, S.: Auto-tuning Performance on Multicore Computers. Ph.D. thesis, EECS Department, University of California, Berkeley (December 2008)

5. Williams, S., Watterman, A., Patterson, D.: Roofline: An insightful visual performance model for floating-point programs and multicore architectures. Communications of the ACM (April 2009)

6. Williams, S., Stralen, B.V., Ligocki, T., Oliker, L., Cordery, M., Lo, L.: Roofline performance model, https://crd.lbl.gov/departments/computer-science/PAR/research/roofline/

7. Tal, A.: Intel software development emulator, https://software.intel.com/en-us/articles/intel-software-development-emulator