Rubyconfindia2018 - GPU accelerated libraries for Ruby
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The document presents an overview of GPU-accelerated libraries for Ruby, focusing on the integration of libraries such as ArrayFire and RBCuda for scientific computing. It highlights the capabilities of these libraries in performing matrix operations and benchmarks their performance against traditional Ruby libraries, demonstrating substantial speed improvements. Future work includes enhancements in image processing, multiple data types, and ongoing development funded by the Ruby Association.
![Af_Array
[1] pry(main)> a = ArrayFire::Af_Array.new 2, [2,2],[1,2,3,4]
No Name Array
[2 2 1 1]
Offsets: [0 0 0 0]
Strides: [1 2 4 4]
1.0000 3.0000
2.0000 4.0000
=> #<ArrayFire::Af_Array:0x000000020aeab8>](https://image.slidesharecdn.com/rubyconfindia20181-180211083840/85/Rubyconfindia2018-GPU-accelerated-libraries-for-Ruby-10-320.jpg)
![Af_Array
[1] pry(main)> a = ArrayFire::Af_Array.new 2, [2,2],[1,2,3,4]
No Name Array
[2 2 1 1]
Offsets: [0 0 0 0]
Strides: [1 2 4 4]
1.0000 3.0000
2.0000 4.0000
=> #<ArrayFire::Af_Array:0x000000020aeab8>](https://image.slidesharecdn.com/rubyconfindia20181-180211083840/85/Rubyconfindia2018-GPU-accelerated-libraries-for-Ruby-11-320.jpg)
![Af_Array
[1] pry(main)> a = ArrayFire::Af_Array.new 2, [2,2],[1,2,3,4]
No Name Array
[2 2 1 1]
Offsets: [0 0 0 0]
Strides: [1 2 4 4]
1.0000 3.0000
2.0000 4.0000
=> #<ArrayFire::Af_Array:0x000000020aeab8>](https://image.slidesharecdn.com/rubyconfindia20181-180211083840/85/Rubyconfindia2018-GPU-accelerated-libraries-for-Ruby-12-320.jpg)
![Af_Array
[1] pry(main)> a = ArrayFire::Af_Array.new 2, [2,2],[1,2,3,4]
No Name Array
[2 2 1 1]
Offsets: [0 0 0 0]
Strides: [1 2 4 4]
1.0000 3.0000
2.0000 4.0000
=> #<ArrayFire::Af_Array:0x000000020aeab8>](https://image.slidesharecdn.com/rubyconfindia20181-180211083840/85/Rubyconfindia2018-GPU-accelerated-libraries-for-Ruby-13-320.jpg)
![[2] pry(main)> b = a + a
No Name Array
[2 2 1 1]
Offsets: [0 0 0 0]
Strides: [1 2 4 4]
2.0000 6.0000
4.0000 8.0000
=> #<ArrayFire::Af_Array:0x000000020625c8>](https://image.slidesharecdn.com/rubyconfindia20181-180211083840/85/Rubyconfindia2018-GPU-accelerated-libraries-for-Ruby-14-320.jpg)
![[1] pry(main)> left = ArrayFire::Af_Array.new 2 , [3,3] , [1, 4, 6, 4, 11 , 2 ,-5, 8, 10]
No Name Array
[3 3 1 1]
1.0000 4.0000 -5.0000
4.0000 11.0000 8.0000
6.0000 2.0000 10.0000
=> #<ArrayFire::Af_Array:0x000000014e56c8>
[2] pry(main)> right = ArrayFire::Af_Array.new 2 , [3,2] , [1, 0, 8, 10, -11, 8]
No Name Array
[3 2 1 1]
1.0000 10.0000
0.0000 -11.0000
8.0000 8.0000
=> #<ArrayFire::Af_Array:0x00000001591db0>](https://image.slidesharecdn.com/rubyconfindia20181-180211083840/85/Rubyconfindia2018-GPU-accelerated-libraries-for-Ruby-15-320.jpg)
![[3] pry(main)> result = ArrayFire::BLAS.matmul(left, right, :AF_MAT_NONE, :AF_MAT_NONE)
No Name Array
[3 2 1 1]
-39.0000 -74.0000
68.0000 -17.0000
86.0000 118.0000
=> #<ArrayFire::Af_Array:0x000000016136f8>](https://image.slidesharecdn.com/rubyconfindia20181-180211083840/85/Rubyconfindia2018-GPU-accelerated-libraries-for-Ruby-16-320.jpg)
![VALUE arf_init(int argc, VALUE* argv, VALUE self)
{
afstruct* afarray;
Data_Get_Struct(self, afstruct, afarray);
dim_t ndims = (dim_t)NUM2LONG(argv[0]);
dim_t* dimensions = (dim_t*)malloc(ndims * sizeof(dim_t));
dim_t count = 1;
for (size_t index = 0; index < ndims; index++) {
dimensions[index] = (dim_t)NUM2LONG(RARRAY_AREF(argv[1], index));
count *= dimensions[index];
}
double* host_array = (double*)malloc(count * sizeof(double));
for (size_t index = 0; index < count; index++) {
host_array[index] = (double)NUM2DBL(RARRAY_AREF(argv[2], index));
}
af_create_array(&afarray->carray, host_array, ndims, dimensions, f64);
return self;
}](https://image.slidesharecdn.com/rubyconfindia20181-180211083840/85/Rubyconfindia2018-GPU-accelerated-libraries-for-Ruby-17-320.jpg)
![vadd_kernel_src = <<-EOS
extern "C" {
__global__ void matSum(int *a, int *b, int *c)
{
int tid = blockIdx.x;
if (tid < 100)
c[tid] = a[tid] + b[tid];
}
}
EOS
f = compile(vadd_kernel_src)
RbCUDA::Driver.run_kernel(f.path)](https://image.slidesharecdn.com/rubyconfindia20181-180211083840/85/Rubyconfindia2018-GPU-accelerated-libraries-for-Ruby-36-320.jpg)
Rubyconfindia2018 - GPU accelerated libraries for Ruby
- 2. About me ● SciRuby Contributor ● Google Summer of Code 2016, 2017 ● Genenetwork project ● Ruby Grant 2017 ● Projects: ○ JRuby port of NMatrix ○ ArrayFire gem ○ RbCUDA
- 4. Why should Python programmers have all the fun ?
- 7. GPU Computing is not easy !
- 10. Af_Array [1] pry(main)> a = ArrayFire::Af_Array.new 2, [2,2],[1,2,3,4] No Name Array [2 2 1 1] Offsets: [0 0 0 0] Strides: [1 2 4 4] 1.0000 3.0000 2.0000 4.0000 => #<ArrayFire::Af_Array:0x000000020aeab8>
- 11. Af_Array [1] pry(main)> a = ArrayFire::Af_Array.new 2, [2,2],[1,2,3,4] No Name Array [2 2 1 1] Offsets: [0 0 0 0] Strides: [1 2 4 4] 1.0000 3.0000 2.0000 4.0000 => #<ArrayFire::Af_Array:0x000000020aeab8>
- 12. Af_Array [1] pry(main)> a = ArrayFire::Af_Array.new 2, [2,2],[1,2,3,4] No Name Array [2 2 1 1] Offsets: [0 0 0 0] Strides: [1 2 4 4] 1.0000 3.0000 2.0000 4.0000 => #<ArrayFire::Af_Array:0x000000020aeab8>
- 13. Af_Array [1] pry(main)> a = ArrayFire::Af_Array.new 2, [2,2],[1,2,3,4] No Name Array [2 2 1 1] Offsets: [0 0 0 0] Strides: [1 2 4 4] 1.0000 3.0000 2.0000 4.0000 => #<ArrayFire::Af_Array:0x000000020aeab8>
- 14. [2] pry(main)> b = a + a No Name Array [2 2 1 1] Offsets: [0 0 0 0] Strides: [1 2 4 4] 2.0000 6.0000 4.0000 8.0000 => #<ArrayFire::Af_Array:0x000000020625c8>
- 15. [1] pry(main)> left = ArrayFire::Af_Array.new 2 , [3,3] , [1, 4, 6, 4, 11 , 2 ,-5, 8, 10] No Name Array [3 3 1 1] 1.0000 4.0000 -5.0000 4.0000 11.0000 8.0000 6.0000 2.0000 10.0000 => #<ArrayFire::Af_Array:0x000000014e56c8> [2] pry(main)> right = ArrayFire::Af_Array.new 2 , [3,2] , [1, 0, 8, 10, -11, 8] No Name Array [3 2 1 1] 1.0000 10.0000 0.0000 -11.0000 8.0000 8.0000 => #<ArrayFire::Af_Array:0x00000001591db0>
- 16. [3] pry(main)> result = ArrayFire::BLAS.matmul(left, right, :AF_MAT_NONE, :AF_MAT_NONE) No Name Array [3 2 1 1] -39.0000 -74.0000 68.0000 -17.0000 86.0000 118.0000 => #<ArrayFire::Af_Array:0x000000016136f8>
- 17. VALUE arf_init(int argc, VALUE* argv, VALUE self) { afstruct* afarray; Data_Get_Struct(self, afstruct, afarray); dim_t ndims = (dim_t)NUM2LONG(argv[0]); dim_t* dimensions = (dim_t*)malloc(ndims * sizeof(dim_t)); dim_t count = 1; for (size_t index = 0; index < ndims; index++) { dimensions[index] = (dim_t)NUM2LONG(RARRAY_AREF(argv[1], index)); count *= dimensions[index]; } double* host_array = (double*)malloc(count * sizeof(double)); for (size_t index = 0; index < count; index++) { host_array[index] = (double)NUM2DBL(RARRAY_AREF(argv[2], index)); } af_create_array(&afarray->carray, host_array, ndims, dimensions, f64); return self; }
- 18. static VALUE arf_matmul(VALUE self, VALUE left_val, VALUE right_val, VALUE left_prop_val, VALUE right_prop_val){ afstruct* left; afstruct* right; afstruct* result = ALLOC(afstruct); Data_Get_Struct(left_val, afstruct, left); Data_Get_Struct(right_val, afstruct, right); af_mat_prop left_mat_prop = arf_mat_type_from_rbsymbol(left_prop_val); af_mat_prop right_mat_prop = arf_mat_type_from_rbsymbol(right_prop_val); af_matmul(&result->carray, left->carray, right->carray, left_mat_prop, right_mat_prop); return Data_Wrap_Struct(CLASS_OF(left_val), NULL, arf_free, result); }
- 19. BLAS functionalities ● Matmult ● Transpose LAPACK functionalities ● Det ● Inverse ● Norm ● Qr ● Cholesky ● Svd ● lu
- 20. Statistics ● Mean ● Median ● Variance
- 21. Benchmarks ● AMD FX 8350 octacore processor ● Nvidia GTX 750Ti GPU ● Double dtype
- 23. 10 X Faster than NMatrix-Ruby-Lapack
- 27. 10,000 X Faster than NMatrix-Ruby
- 31. 100,000 X Faster than NMatrix-Ruby-BLAS
- 34. RbCUDA
- 35. GPU Array ● Generic pointer used to handle an array of elements on the GPU. ● Memory copying from CPU to GPU and vice-versa. ● Interfaced with NMatrix and NArray
- 36. vadd_kernel_src = <<-EOS extern "C" { __global__ void matSum(int *a, int *b, int *c) { int tid = blockIdx.x; if (tid < 100) c[tid] = a[tid] + b[tid]; } } EOS f = compile(vadd_kernel_src) RbCUDA::Driver.run_kernel(f.path)
- 37. ● CuBLAS ● CuSolver ● CuRand
- 38. Benchmarks ● AMD FX 8350 octacore processor ● Nvidia GTX 750Ti GPU ● Double dtype
- 40. 1,000,000 X Faster than NMatrix-Ruby-BLAS
- 43. Future Work ● Image Processing APIs and Indexers ● Multiple dtypes ● RbCUDA is under active development. ● Project RbCUDA is being funded by Ruby Association (Ruby Grant 2017)
- 44. Hobby Projects 1. Synapse : A Ruby first Deep Learning Library 2. Ninjaplot : A plotting library
- 45. ● https://github.com/arrayfire/arrayfire-rb ● https://github.com/prasunanand/rbcuda ● https://github.com/prasunanand/synapse ● https://github.com/prasunanand/ninjaplot Contributions are Welcome!
- 46. Acknowledgements 1. Pjotr Prins 2. Pradeep Garigipati 3. Kenta Murata
- 47. Thanks! Github: prasunanand Twitter: @prasun_anand Blog: prasunanand.com
- 48. Questions