Introduction to CUDA Programming Histograms and Sparse Array Multiplication Andreas Moshovos Winter 2009 Based on documents from: NVIDIA & Appendix A of the New P&H Book

Histogram E.g., Given an image calculate this: Distribution of values

Sequential Algorithm for (int i = 0; i < BIN_COUNT; i++) result[i] = 0; for (int i = 0; i < dataN; i++) result[data[i]]++; The challenge is that the write access pattern is data dependent No ordering of accesses to memory

Data Race Thread 1Thread 2 –X++X++ Start with X = 10 X++ is really –tmp = Read x –Increment tmp –Write tmp into x Thread 1Thread 2 –Read 10Read 10 –10+1 = = 11 –X = 11X = 11 X = 11 and not 12

Parallel Strategy Distribute work across multiple blocks –Divide input data to blocks Each block process its own portion –Multiple threads, one per image pixel? #pixels / #threads per thread –Produces a partial histogram Could produce multiple histograms One per thread  no ordering problems here Merge all partial histograms –Produces the final histogram

Data Structures and Access Patterns result[data[i]]++; Data[]: –We control: Can be accessed sequentially –Each element accessed only once Result[]: –Access is data-dependent –Each element may be accessed multiple times Data[] in global memory Result[] in shared memory

Sub-Histograms How many sub-histograms can we fit in shared memory? –Input value range: 0-255, 1 byte –Each histogram needs 256 entries –How many bytes per entry? That’s data dependent –Let’s assume 32-bits or 4 bytes 16KB (shared memory) / (256 x 4) (histogram) –16 sub-histograms at any given point of time –If one per thread then we have less than a warp Let’s try one histogram per block –many threads per block –Ordering problem persists but within a block

Partial Histogram Data Structure Array in shared memory One per block One column per possible pixel value

Algorithm Overview Step 1: –Initialize partial histogram –Each thread: s_Hist[index] = 0 index += threads per block Step 2: –Update histogram –Each thread: read data[index] update s_Hist[]  conflicts possible index += Total number of threads Step 3: –Update global histogram –Each thread read s_hist[index] update global histogram index += threads per block

Simultaneous Updates? Threads in a block: –update s_Hist[] All threads: –update global histogram Without special support this becomes: –register X = value of A –X ++ –A = register X This is a read-modify-write sequence

The problem with simultaneous updates What if we do each step individually –r10 = mem[100]10r100 = mem[100] 10 –r10++11r –mem[100] = r1011mem[100] = r But we really wanted 12 What if we had 32 threads running in parallel? Start with 10 we would want: –We may still get 11 Need to think about this: –Special support: Atomic operations

Atomic Operations Read-Modify-Write operations that are guaranteed to happen “atomically” –Produces the same result as if the sequence executed in isolation in time –Think of it as “serializing the execution” of all atomics –This is not what necessarily happens This is how you should think about them

Atomic Operations Supported both in Shared and Global memory Example: –atomicAdd (pointer, value) –does: *pointer += value Atomic Operations –Add, Sub, Inc, Dec –Exch, Min, Max, CAS –Bitwise: And, Or, Xor Work with (unsigned) integers Exch works with single FP as well

atomicExch, atomicMin, atomicMax, atomicCAS atomicExch (pointer, value) –tmp = * pointer –*pointer = value –return tmp atomicMin (pointer, value) (max is similar) –tmp = *pointer –if (*pointer < value) *pointer = value –return tmp atomicCAS (pointer, value1, value2) –tmp = *pointer –if (*pointer == value1) *pointer = value2 –return tmp

atomicInc, atomicDec atomicInc (pointer, value) –tmp = *pointer –if (*pointer < value) (*pointer)++ –else *pointer = 0 –return tmp atomicDec (pointer, value) –tmp = *pointer –if (*pointer == 0 || *pointer > value) *pointer = value –else (*pointer)-- –return tmp Allow for warp-around work queues

atomicAnd, atomicOr, atomicXOR atomicAnd (pointer, value) –tmp = *pointer –*pointer = *pointer & value –return tmp Others similar

CUDA Implementation - Declarations __global__ void histogram256Kernel (uint *d_Result, uint *d_Data, int dataN){ //Current global thread index const int globalTid = blockIdx.x * blockDim.x + threadIdx.x; //Total number of threads in the compute grid const int numThreads = blockDim.x * gridDim.x; __shared__ uint s_Hist[BIN_COUNT];

Clear partial histogram buffer //Clear shared memory buffer for current block before processing for (int pos = threadIdx.x; pos < BIN_COUNT; pos += blockDim.x) s_Hist[pos] = 0; __syncthreads (); // All threads finished clearing out the // histogram

Generate partial histogram for (int pos = globalTid; pos < dataN; pos += numThreads){ uint data4 = d_Data[pos]; // coalesced // shared memory is word interleaved // read four pixels per thread with a load word atomicAdd (s_Hist + (data4 >> 0) & 0xFFU, 1); atomicAdd (s_Hist + (data4 >> 8) & 0xFFU, 1); atomicAdd (s_Hist + (data4 >> 16) & 0xFFU, 1); atomicAdd (s_Hist + (data4 >> 24) & 0xFFU, 1); // we are not using atomicInc which has a more // complex structure that atomicAdd } __syncthreads();

Merge partial histogram with global histogram for (int pos = threadIdx.x; pos < BIN_COUNT; pos += blockDim.x){ atomicAdd(d_Result + pos, s_Hist[pos]); // these operate on global memory }

Code overview __global__ void histogram256Kernel (uint *d_Result, uint *d_Data, int dataN){ const int globalTid = blockIdx.x * blockDim.x + threadIdx.x; const int numThreads = blockDim.x * gridDim.x; __shared__ uint s_Hist[BIN_COUNT]; for (int pos = threadIdx.x; pos < BIN_COUNT; pos += blockDim.x) s_Hist[pos] = 0; __syncthreads (); for (int pos = globalTid; pos < dataN; pos += numThreads){ uint data4 = d_Data[pos]; // coalesced atomicAdd (s_Hist + (data4 >> 0) & 0xFFU, 1); atomicAdd (s_Hist + (data4 >> 8) & 0xFFU, 1); atomicAdd (s_Hist + (data4 >> 16) & 0xFFU, 1); atomicAdd (s_Hist + (data4 >> 24) & 0xFFU, 1); } __syncthreads(); for (int pos = threadIdx.x; pos < BIN_COUNT; pos += blockDim.x) atomicAdd(d_Result + pos, s_Hist[pos]); }

Discussion s_Hist updates –Conflicts in shared memory –Data Dependent –16-way conflicts possible and likely Is there an alternative? –One histogram per thread? Not enough shared memory –Load data in shared memory Each thread produces a portion of the s_Hist that maps onto the same bank?

Warp Vote Functions WARP, not block, grid, etc. ** WARP ** int __all (int predicate); –evaluates predicate for all threads of the warp and returns non-zero (TRUE) if and only if predicate evaluates to non-zero (TRUE) for all of them. int __any (int predicate); –evaluates predicate for all threads of the warp and returns non-zero (TRUE) if and only if predicate evaluates to non-zero (TRUE) for any of them.

Warp Vote Functions Example Original code: for (int pos = threadIdx.x; pos < BIN_COUNT; pos += blockDim.x){ atomicAdd(d_Result + pos, s_Hist[pos]); Modified w/ __any (): –Update only if there are non-zero values for (int pos = threadIdx.x; pos < BIN_COUNT; pos += blockDim.x){ if (__any (s_Hist[pos] != 0) ) atomicAdd(d_Result + pos, s_Hist[pos]); Modified w/ __all (): for (int pos = threadIdx.x; pos < BIN_COUNT; pos += blockDim.x){ if (!__all (s_Hist[pos] == 0) ) atomicAdd(d_Result + pos, s_Hist[pos]);

Fence Primitives vs. Syncthreads __syncthreads() Waits until: –all threads reach this point and All threads must execute this  otherwise we have deadlock –all their global and shared memory accesses made up to __syncthreads() are visible to all threads within the block. __threadfence_block() Waits for all global and shared memory accesses made by the thread before the __threadfence_block() to become visible to: –All threads in the block. –Not all threads have to do this __threadfence() Waits for all global and shared memory accesses made by the thread before the __threadfence() to become visible to: –All threads in the device for global memory –All threads in the block for shared memory –Not all threads have to execute this

Sparse Matrix Multiplication Sparse Matrix N x N: –number of non-zero entries m is only a small fraction of the total Representation goal: –store only non-zero entries Typically: –m = O(N)

Compressed Sparse Row Representation Av[]: –Array values in row-major order Aj[]: –Column for corresponding Av[] entry Ap[]: –row i extends from indexes Ap[i] to Ap[i+1] -1 in Av[] and Aj[]

Matrix x Vector: y = Ax Av Aj Ap x x x x Ax

Single Row Multiply Produces an entry of the result vector float multiply_row (uint rowsize, uint *Aj, // column indices for row float *Av, // nonzero entries for row float *xl // the RHS vector { float sum = 0; for (uint column=0; column<rowsize; column++) sum = Av[column] * x[ Aj[column] ] ; return sum; } Av Aj Ap

Serial Code void csrmul_serial (uint *Ap, uint *Aj, float *Av, uint num_rows, float *x, float *y) { for (uint row=0; row<num_rows; row++) { uint row_begin = Ap[row]; uint row_end = Ap[row + l]; y[row] = multiply_row ( row_end - row_begin, Aj+row_begin, Av+row_begin, x); } Av Aj Ap02257

CUDA Strategy Assume that there are many rows One thread per row

CUDA Kernel __global__ void csrmul_kernel (uint *Ap, uint *Aj, float *Av, uint num_rows, float *x, float *y) uint row = blockIdx.x * blockDim.x + threadIdx.x; uint row_begin = Ap[row]; uint row_end = Ap[row+1]; y[row] = multiply_row (row_end – row_begin, Aj + row_begin, Av + row_begin, x); }

Discussion We are not using shared memory –all are in global memory Claim: –rows using x[i] will be rows near row i –Nature of computations using sparse matrices Block processing rows i through j –cache x[i] through x[j] –load from shared memory if possible Unroll multiply_row() Fetch Ap[row+1] from adjacent thread

CSR multiplication using shared memory __global__ void csrmul_cached( uint *Ap, uint *Aj, float *Av, uint num_rows,float *x, float *y) { __shared__ float cache[blocksize]; uint block_begin = blockIdx.x * blockDim.x; uint block_end = block_begin + blockDim.x; uint row = block_begin + threadIdx.x: if (row < num_rows) cache[threadIdx.x] = x[row]; __syncthreads(); if (row < num_rows) // for left over threads at the end { uint row_begin = Ap[row]; uint row_end = Ap[row + l] ; float sum = 0, x_j ; for (uint col=row_begin; col < row_end; col++ ) { uint j = Aj[col]; // Fetch x_j from our cache when possible if (j >= block_begin && j < block_end ) x_j = cache [j – block_begin]; else x_j = x [j]; sum += Av[col] * x_j ; } y[row] = sum; }