x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map ooooo ooooooo ooooooooo ooooo oooooooooooooo OpenCL for x86 CPU and Intel MIC J-W I- ■ I " "V in Fihpovic Fall 2021 Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map •oooo ooooooo ooooooooo ooooo oooooooooooooo x86 CPU Architecture Common features of (nearly all) modern x86 processors • core is complex, out-of-order instruction execution, large cache • multiple cache coherent cores in single chip • vector instructions (MMX, SSE, AVX) a NU MA for multi-socket systems Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map O0OOO ooooooo ooooooooo ooooo oooooooooooooo OpenCL Device Compute Device k G obrfl.'CorMdn: Memory Data Ca-; "? Global Memory Constant Memory Compute Unit i ■ ■ ■ Compute Unit n Private Memory 1 Private Memory m Private Memory 1 Private Memory m 1 ... I 1 t PEi PEi PE m t ■ ■ t ■ ■ Local Memory 1 Local Memory n • 1 ■ > Jih' Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map oo«oo ooooooo ooooooooo ooooo oooooooooooooo CPU and OpenCL The projection of CPU HW to OpenCL model • CPU cores are compute units • vector ALUs are processing elements • so the number of work-items running in lock-step is determined by instruction set (e.g., SSE, AVX) and data type (e.g., float, double) • one or more work-groups create a CPU thread o the number of work-groups should be at least equal to the number of cores • higher number of work-groups allows to better workload balance (e.g., what if we have eight work-groups at six-core CPU?), but creates overhead • work-items form serial loop, which may be vectorized Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map ooo»o ooooooo ooooooooo ooooo oooooooooooooo Implicit and Explicit Vectorization Implicit vectorization • we write scalar code (similarly as for NVIDIA and AMD GCN) • the compiler generates vector instructions from work-items (creates loop over work-items and vectorizes this loop) o better portability (we do not care about vector size and richness of vector instruction set) • supported by Intel OpenCL, AMD OpenCL does not support it yet Explicit vectorization • we use vector data types in our kernels • more complex programming, more architecture-specific • potentially better performance (we do not rely on compiler ability to vectorize) Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map oooo» ooooooo ooooooooo ooooo oooooooooooooo Differences from GPU Images • CPU does not support texture units, so they are emulated better to not use... Local memory o no special HW at CPU • brings overhead (additional memory copies) • but it is meaningful to use memory pattern common for using local memory, as it improves cache locality Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map OOOOO «000000 ooooooooo ooooo oooooooooooooo Intel MIC What is MIC? • Many Integrated Core Architecture • originated in Intel Larrabee project (x86 graphic card) Existing hardware • Knights Corner (KNC) and Knights Landing (KNL) generation • large number of x86 cores • cores are connected by bi-directional ring bus (KNC) or mesh (KNL) 9 cache-coherent system • connected to high-throughput memory Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map OOOOO O0OOOOO ooooooooo ooooo oooooooooooooo KNC Processor GDDR5 GDDR5 GDDR5 GDDR5 SBOX PCIe v2.0 controller, DMA engines CORE L2 OOO CORE L2 GBOX (memory controller) Core Ring Interconnect (CRI) -«-DATA-* *■ ADDRESS -COHERENCE-* L2 CORE O O O L2 CORE GBOX (memory controller) GDDR5 GDDR5 GDDR5 GDDR5 Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map ooooo oo«oooo ooooooooo ooooo oooooooooooooo KNL Processor Jiri Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map OOOOO OOO0OOO ooooooooo ooooo oooooooooooooo Intel MIC MIC core • relatively simple, KNC in-order, KNL based on Atom Airmont • use hyperthreading (4 threads per core) • needs to be used to exploit full performance on KNC • fully cache coherent, 32+32 KB LI cache (l+D), 512KB L2 cache • contain wide vector units (512-bit vectors) • predicated execution • gather/scatter instructions • transcendentals Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map ooooo oooo«oo ooooooooo ooooo oooooooooooooo Current Hardware Xeon Phi • product based on MIC architecture • bootable processor, or PCI-E card with dedicated memory • runs its own operating system Xeon Phi 7210 • 64 x86 cores at 1.3 GHz • 16GB HBM RAM + DDR4 RAM up to 384GB • 2.25TFIops DP, 4.5TFIops SP • 450GB/sec HBM, 102GB/s DDR4 memory bandwidth Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map ooooo ooooo«o ooooooooo ooooo oooooooooooooo Programming Models Native programming model (KNC) • we can execute the code directly at accelerator • after recompilation, we can use the same code as for CPU • programming via OpenMP, MPI Offload programming model (KNC) • application is executed at host • code regions are offloaded to accelerator, similarly as in the case of GPUs by using #pragma offload with intel tools • by using OpenCL KNL is host processor. Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map ooooo oooooo* ooooooooo ooooo oooooooooooooo MIC and OpenCL The projection of MIC HW to OpenCL programming model is very similar to CPU case • work-groups creates threads • work-items creates iterations of vectorized loops • higher number of work-items due to wider vectors • less sensitive to divergence and uncoalesced memory access due to richer vector instruction set • high need of parallelism • e.g., 64 cores executes 256 threads Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map OOOOO OOOOOOO «00000000 ooooo oooooooooooooo OpenCL Optimization for CPU and MIC We will discuss optimizations for CPU and MIC together • many common concepts • differences will be emphasized Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU ooooo Intel MIC ooooooo Optimization O0OOOOOOO Reduction ooooo Electrostatic Potential Map oooooooooooooo Parallelism How to set a work-group size? 9 we do not need high parallelism to mask memory latency • but we need enough work-items to fill vector width (if implicit vectorization is employed) • the work-group size should be divisible by vector length, it can by substantially higher, if we don't use local barriers • Intel recommends 64-128 work-items without synchronizations and 32-64 work-items with synchronizations • general recommendation, needs experimenting . .. • we can let a compiler to choose the work-group size How many work-groups? • ideally multiple of (virtual) cores • be aware of NDRange tile effect (especially at MIC) Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map ooooo ooooooo oo«oooooo ooooo oooooooooooooo Thread-level Parallelism Task-scheduling overhead <* overhead of scheduling large number of threads o issue mainly on MIC (CPU has too low cores) • problematic for light-weight work groups • low workload per work-item • small work-groups • can be detected by profiler easily Barriers overhead • no HW implementation of barriers, so they are expensive higher slowdown on MIC Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map ooooo ooooooo ooo«ooooo ooooo oooooooooooooo Vectorization Branches • if possible, use uniform branching (whole work-group follows the same branch) * consider the difference • if (get_global_id(0) == 0) • if (kernel.arg == 0) 9 divergent branches • can forbid vectorization • can be masked (both then and else branches are executed) Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map OOOOO OOOOOOO OOOO0OOOO ooooo oooooooooooooo Vectorization Scatter/gather • supported mainly on MIC • for non-consecutive memory access, compiler tries to generate scatter/gatter instructions • instructions use 32-bit indices • get_global_id() returns size_t (64-bit) • we can cast indices explicitly • avoid pointer arithmetics, use array indexing • more transparent for the compiler Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map ooooo ooooooo ooooo«ooo ooooo oooooooooooooo Memory Locality Cache locality • the largest cache dedicated to core is L2 • cache blocking - create work-groups using memory regions fitting into LI, or not exceeding L2 cache AoS • array of structures o more efficient for random access SoA • structure of arrays o more efficient for consecutive access Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map ooooo ooooooo oooooo«oo ooooo oooooooooooooo Memory Access Memory access pattern • consecutive memory access is the most efficient in both architectures • however, there are differences KNC is in-order, so the memory access efficiency heavily depends on prefetching, which is more successful for consecutive access • CPU does not support vector gather/scatter Alignment • some vector instructions require alignment • IMCI (MIC): 64-byte • AVX: no requirements • SSE: 16-byte pad innermost dimension of arrays Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map OOOOO OOOOOOO OOOOOOO0O ooooo oooooooooooooo Memory Access Prefetching on KNC • prefetching is done by HW and by SW • generated by the compiler • also can be explicitly programmed (function void prefetch(const __global gentype *p, size_t num_e 1 ement s)) • explicit prefetching helps, e.g., in irregular memory access pattern Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map ooooo ooooooo oooooooo* ooooo oooooooooooooo Memory Access False sharing • accessing different addresses in the same cache line from several threads • cache line has 64 bytes on modern Intel processors • brings significant penalty <* padding is the solution... Concurrent R/W access to the same address o it is better to create local copies and merge them when necessary (if possible) • reduces also synchronization Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map OOOOO OOOOOOO OOOOOOOOO «0000 oooooooooooooo Vector reduction Rewritten CUDA version • uses very similar concept as was demonstrated in former lecture, but run in constant number of threads q reaches nearly peak theoretical bandwidth on both NVIDIA and AMD GPUs Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map ooooo ooooooo ooooooooo otooo oooooooooooooo Reduction for GPUs (1/2) kernel void reduce(__global const int* in, __global int* out, unsigned int n, __local volatile int *buf) { unsigned int tid = get_local_id(0 ) ; unsigned int i = get_group_id(0)*(get_local_size(0)*2) + get_local_id(0); unsigned int gridSize = 256*2*get_num_groups(0 ) ; buf [tid] = 0; while (i < n) { buf [tid] += in[i]; if (i + 256 < n) buf [tid] += in[i+256]; i += gridSize; } barrier(CLK_LOCAL_MEM_FENCE); Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map ooooo ooooooo ooooooooo oo«oo oooooooooooooo Reduction for GPUs (2/2) //XXX hard optimization for 256—thread work groups if (tid < 128) buf[tid] += buf[tid + 128]; barrier(CLK_LOCAL_MEM_FENCE); if (tid < 64) buf [tid] += buf[tid + 64]; barrier(CLK_LOCAL_MEM_FENCE); //XXX hard optimization for 32—bit warp size //XXX problematic on new NVIDIA HW if (tid < 32) { buf [tid] += buf [tid + 32] buf [tid] += buf [tid + 16] buf [tid] += buf [tid + 8]; buf [tid] += buf [tid + 4]; buf [tid] += buf [tid + 2]; buf [tid] += buf [tid + i]; } if (tid = 0) atomic.add(out, buf [0]); □ ► < if? ► < ^ ► < ^ ► 1 -0 0,0 Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map OOOOO OOOOOOO OOOOOOOOO OOO0O oooooooooooooo Vector reduction Execution of GPU code on CPU and Phi • difficult to vectorize • overhead of local reduction, which is not necessary Optimizations for CPU and MIC • the simplest solution is to use only necessary amount of parallelism 9 work-groups of one vectorized work-item Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map ooooo ooooooo ooooooooo oooo* oooooooooooooo Reduction for CPU and MIC kernel void reduce („global const intl6* in, „global int* out, const unsigned int n, const unsigned int chunk) { unsigned int start = get_global_id(0)* chunk; unsigned int end = start + chunk ; if (end > n) end = n; intl6 tmp = (0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0); for (int i = start/16; i < end/16; i++) tmp += in[i] ; int sum = tmp . sO + tmp . si + tmp . s2 + tmp . s3 + tmp . s4 + tmp.s5 + tmp.s6 + tmp.s7 + tmp.s8 + tmp.s9 + tmp.sa + tmp.sb + tmp.sc + tmp.sd + tmp.se + tmp.sf ; atomic_add(out, sum); Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map OOOOO OOOOOOO OOOOOOOOO OOOOO «0000000000000 Electrostatic Potential Map Important problem from computational chemistry 9 we have a molecule defined by position and charges of its atoms • the goal is to compute charges at a 3D spatial grid around the molecule In a given point of the grid, we have Wj J Where wj is charge of the j-th atom, r,y is Euclidean distance between atom j and the grid point / and eo is vacuum permittivity. Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map ooooo ooooooo ooooooooo ooooo o«oooooooooooo Algorithm Analysis Parallelization • each grid point can be processed in parallel • not practical to parallelize loop going over atoms (reduction) Performance bound of the naive algorithm • 11 arithmetic operations per one atom per grid point • atom's data require 16 bytes (4 floats - Cartesian position and charge) • computation for one grid point is memory-bound • caches maintain locality for multiple grid points (atom reads are synchronous) Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map OOOOO OOOOOOO OOOOOOOOO OOOOO OO0OOOOOOOOOOO Improving the Algorithm We can compute a grid per 2D slices • enough parallelism • distance in z-dimension can be precomputed (stored instead of z-dimension of atom's data) • reduce number of arithmetic operations per atom per grid point to 9 Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map OOOOO OOOOOOO OOOOOOOOO OOOOO OOO0OOOOOOOOOO Implementation int xlndex = get_global_id(0 ) ; int ylndex = get_global_id(1) ; int outlndex = get_global_size(0) * ylndex + xlndex; float coordX = gridSpacing * xlndex; float coordY = gridSpacing * ylndex; float energyValue = O.Of; for (int i = 0; i < numberOfAtoms; i++) { float dX = coordX — atomlnfo[i].x; float dY = coordY — atomlnfo[i].y; energyValue += atomlnfo[i].w * native_rsqrt(dX*dX + dY*dY + atomlnfo[i].z ) ; } energyGrid[out Index] += energyValue; Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map ooooo ooooooo ooooooooo ooooo oooo«ooooooooo Performance Let's set slice size to 512 x 512, number of atoms to 4096, WG size to 16 x 16, and measure the performance in number of atoms evaluated per second. Code 2xCPU MIC GPU slices 25.8Geval/s 48.1Geval/s 45.0Geval/s Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map OOOOO OOOOOOO OOOOOOOOO OOOOO 00000*00000000 Performance Let's optimize WG size • 8 x 2 for CPU, 8 x 1 for MIC, 16 x 4 for GPU Code 2xCPU MIC GPU slices 25.8Geval/s 48.1Geval/s 45.0Geval/s optimized WG 26.1Geval/s 54.4Geval/s 45.8Geval/s Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map OOOOO OOOOOOO OOOOOOOOO OOOOO OOOOOO0OOOOOOO Removing Redundancy Are there any redundant work among Wis? • Wis in the same warp/vector read the same atom data • Wis in the same row compute the same y-distance • redundancy removing critical for GPU, but may also improve performance on CPU and MIC (if compiler fails to remove invariant code) We can assign more work per Wl • "unrolling of the outer (parallelized) loop", so a Wl computes several grid points at a row • increases private memory locality (atom data are used for more grid points) • removes some redundant computation of y-distance • reduces strong scaling, uses more registers Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map OOOOO OOOOOOO OOOOOOOOO OOOOO OOOOOOO0OOOOOO Performance We have tested from 1 to 8 grid points and re-optimize WG size. • unroll 8x for CPU, 2x for MIC and 8x for GPU Code 2xCPU MIC GPU slices 25.8Geval/s 48.1Geval/s 45.0Geval/s optimized WG 26.1Geval/s 54.4Geval/s 45.8Geval/s unrolling 54.5Geval/s 60.9Geval/s 162.0 Geval/s Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map OOOOO OOOOOOO OOOOOOOOO OOOOO OOOOOOOO0OOOOO Memory Access Optimization CPU and MIC often prefers SoA • we can split x,y, z-dimensions and charge w into separate arrays GPU caches global memory in L2 cache only • we can use constant memory for atom data Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map OOOOO OOOOOOO OOOOOOOOO OOOOO ooooooooo«oooo Performance We have tested from 1 to 8 grid points and re-optimize WG size. • CPU and MIC prefers SoA, GPU prefers constant memory (more visible effect if unrolling is disabled) Code 2xCPU MIC GPU slices 25.8Geval/s 48.1Geval/s 45.0 Geval/s optimized WG 26.1Geval/s 54.4Geval/s 45.8 Geval/s unrolling 54.5Geval/s 60.9Geval/s 162.0 Geval/s optimized mem. 60.2Geval/s 61.1 Geval/s 164.9 Geval/s Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map ooooo ooooooo ooooooooo ooooo oooooooooo«ooo Manual Vectorization Vectorization of memory access • we pack atoms data into vectors (both in SoA and AoS) o usable to enforce vectorized data access Vectorized computation • we read vectorized data and perform vectorized computation in each Wl Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map OOOOO OOOOOOO OOOOOOOOO OOOOO OOOOOOOOOOO0OO Performance We have tested using vector from size 2 to size 8. • CPU prefers to not vectorize, MIC prefers SoA with vector size 4 and scalar computation, GPU prefers scalar computation with AoS using vector size 8 (i.e. two atoms are packed into single vector) Code 2xCPU MIC GPU slices 25.8Geval/s 48.1Geval/s 45.0 Geval/s optimized WG 26.1Geval/s 54.4Geval/s 45.8 Geval/s unrolling 54.5Geval/s 60.9Geval/s 162.0 Geval/s optimized mem. 60.2Geval/s 61.1 Geval/s 164.9 Geval/s vectorized 62.4Geval/s 168.3 Geval/s < r5> ► < = > < ► -E -O O Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map OOOOO OOOOOOO OOOOOOOOO OOOOO 000000000000*0 Performance without squere root The performance of MIC is quite low and optimizations does not improve it • slower implementation of native_rsqrt • depsite it leads to uncorrect algorithm, we have tested performance with removed reciprocal square root Jin Filipovic OpenCL for x86 CPU and Intel MIC x86 CPU Intel MIC Optimization Reduction Electrostatic Potential Map ooooo ooooooo ooooooooo ooooo ooooooooooooo* Performance without squere root Code 2xCPU MIC GPU slices 30.0Geval/s 103.8 Geval/s 43.6 Geval/s optimized WG 30.6 Geval/s 114.3 Geval/s 43.8 Geval/s unrolling 68.3 Geval/s 148.9 Geval/s 221.8 Geval/s optimized mem. 70.9 Geval/s 159.3 Geval/s 260.0 Geval/s vectorized 175.4 Geval/s 266.4 Geval/s Jin Filipovic OpenCL for x86 CPU and Intel MIC