Copperhead: A Python-like Data Parallel Language & Compiler

1. Bryan Catanzaro, UC Berkeley Michael Garland, NVIDIA Research Kurt Keutzer, UC Berkeley Copperhead: A Python-like Data Parallel Language & Compiler Universal Parallel Computing Research Center University of California, Berkeley

2. Intro to CUDA Overview Multicore/Manycore SIMD Programming with millions of threads

3. The CUDA Programming Model • CUDA is a recent programming model, designed for • Manycore architectures • Wide SIMD parallelism • Scalability • CUDA provides: • A thread abstraction to deal with SIMD • Synchronization & data sharing between small groups of threads • CUDA programs are written in C + extensions • OpenCL is inspired by CUDA, but HW & SW vendor neutral • Programming model essentially identical

4. Multicore and Manycore Multicore Manycore • Multicore: yoke of oxen • Each core optimized for executing a single thread • Manycore: flock of chickens • Cores optimized for aggregate throughput, deemphasizing individual performance

5. Multicore & Manycore, cont. Core i7 GTX285

6. SIMD: Neglected Parallelism • It is difficult for a compiler to exploit SIMD • How do you deal with sparse data & branches? • Many languages (like C) are difficult to vectorize • Fortran is somewhat better • Most common solution: • Either forget about SIMD • Pray the autovectorizer likes you • Or instantiate intrinsics (assembly language) • Requires a new code version for every SIMD extension

7. What to do with SIMD? 4 way SIMD 16 way SIMD • Neglecting SIMD in the future will be more expensive • AVX: 8 way SIMD, Larrabee: 16 way SIMD • This problem composes with thread level parallelism

8. CUDA • CUDA addresses this problem by abstracting both SIMD and task parallelism into threads • The programmer writes a serial, scalar thread with the intention of launching thousands of threads • Being able to launch 1 Million threads changes the parallelism problem • It’s often easier to find 1 Million threads than 32: just look at your data & launch a thread per element • CUDA is designed for Data Parallelism • Not coincidentally, data parallelism is the only way for most applications to scale to 1000(+) way parallelism

9. Hello World

10. CUDA Summary CUDA is a programming model for manycoreprocessors It abstracts SIMD, making it easy to use wide SIMD vectors It provides good performance on today’s GPUs In the near future, CUDA-like approaches will map well to many processors & GPUs CUDA encourages SIMD friendly, highly scalable algorithm design and implementation

11. A Parallel Scripting Language • What is a scripting language? • Lots of opinions on this • I’m using an informal definition: • A language where performance is happily traded for productivity • Weak performance requirement of scalability • “My code should run faster tomorrow” • What is the analog of today’s scripting languages for manycore?

12. Data Parallelism Assertion: Scaling to 1000 cores requires data parallelism Accordingly,manycore scripting languages will be data parallel They should allow the programmer to express data parallelism naturally They should compose and transform the parallelism to fit target platforms

13. Warning: Evolving Project Copperhead is still in embryo We can compile a few small programs Lots more work to be done in both language definition and code generation Feedback is encouraged

14. Copperhead = Cu + python • Copperhead is a subset of Python, designedfor data parallelism • Why Python? • Extant, well accepted high level scripting language • Free simulator(!!) • Already understands things like map and reduce • Comes with a parser & lexer • The current Copperhead compiler takes a subset of Python and produces CUDA code • Copperhead is not CUDA specific, but current compiler is

15. Copperhead is not Pure Python Python Copperhead • Copperhead is not for arbitrary Python code • Most features of Python are unsupported • Copperhead is compiled, not interpreted • Connecting Python code & Copperhead code will require binding the programs together, similar to Python-C interaction • Copperhead is statically typed

16. Saxpy: Hello world defsaxpy(a, x, y): returnmap(lambda xi, yi: a*xi + yi, x, y) • Some things to notice: • Types are implicit • The Copperhead compiler uses a Hindley-Milner type system with typeclasses similar to Haskell • Typeclasses are fully resolved in CUDA via C++ templates • Functional programming: • map, lambda (or equivalent in list comprehensions) • you can pass functions around to other functions • Closure: the variable ‘a’ is free in the lambda function, but bound to the ‘a’ in its enclosing scope

17. Type Inference, cont. c = a + b + : (Num0, Num0) > Num0 A145 A52 A207 c = a + b Num52 Num52 Num52 Copperhead includes function templates for intrinsics likeadd, subtract, map, scan, gather Expressions are mapped against templates Every variable starts out with a unique generic type, then types are resolved by union find on the abstract syntax tree Tuple and function types are also inferred

18. Data parallelism • Copperhead computations are organized around data parallel arrays • map performs a “forall” for each element in an array • Accesses must be local • Accessing non-local elements is done explicitly • shift, rotate, or gather • No side effects allowed

19. Copperhead primitives • map • reduce • Scans: • scan, rscan, segscan, rsegscan • exscan, exrscan, exsegscan, exrsegscan • Shuffles: • shift, rotate, gather, scatter

20. Implementing Copperhead Module( None, Stmt( Function( None, 'saxpy', ['a', 'x', 'y'], 0, None, Stmt( Return( CallFunc( Name('map'), Lambda( ['xi', 'yi'], 0, Add( Mul( Name('a'), Name('xi') ), Name('yi') ) ), Name('x'), Name('y'), None, None ) ) ) ) ) ) defsaxpy(a, x, y): returnmap(lambda xi, yi: a*xi + yi, x, y) The Copperhead compiler is written in Python Python provides its own Abstract Syntax Tree Type inference, code generation, etc. is done by walking the AST

21. Compiling Copperhead to CUDA • Every Copperhead function creates at least one CUDAdevice function • Top level Copperhead functions create a CUDAglobalfunction, which orchestrates thedevicefunction calls • Theglobalfunction takes care of allocating shared memory and returning data (storing it to DRAM) • Global synchronizations are implemented through multiple phases • All intermediate arrays & plumbing handled by Copperhead compiler

22. Saxpy Revisited defsaxpy(a, x, y): returnmap(lambda xi, yi: a*xi + yi, x, y) template<typename Num> __device__ Num lambda0(Num xi, Num yi, Num a) { return ((a * xi) + yi); } template<typename Num>__device__ void saxpy0Dev(Array<Num> x, Array<Num> y, Num a, uint _globalIndex, Num& _returnValueReg) { Num _xReg, yReg; if (_globalIndex < x.length) _xReg= x[_globalIndex]; if (_globalIndex < y.length) _yReg= y[_globalIndex]; if (_globalIndex < x.length) _returnValueReg= lambda0<Num>(_xReg, _yReg, a); } template<typename Num>__global__ void saxpy0(Array<Num> x, Array<Num> y, Num a, Array<Num> _returnValue) { uint _blockMin = IMUL(blockDim.x, blockIdx.x); uint _blockMax = _blockMin + blockDim.x; uint _globalIndex = _blockMin + threadIdx.x; Num _returnValueReg; saxpy0Dev(x, y, a, _globalIndex, _returnValueReg); if (_globalIndex < _returnValue.length) _returnValue[_globalIndex] = _returnValueReg; }

23. Phases phase 0 phase 1 • Scan phase 0 phase 1 phase 2 Reduction

24. Copperhead to CUDA, cont. B = reduce(map(A)) D = reduce(map(C)) phase 0 phase 1 • Compiler schedules computations into phases • Right now, this composition is done greedily • Compiler tracks global and local availability of all variables and creates a phase boundary when necessary • Fusing work into phases is important for performance

25. Copperhead to CUDA, cont. • Shared memory used only for communicating between threads • Caching unpredictable accesses (gather) • Accessing elements with a uniform stride (shift & rotate) • Each device function returns its intermediate results through registers

26. Split defsplit(input, value): flags = map(lambda a: 1 if a <= value else 0, input) notFlags = map(lambda a: not a, flags) leftPositions = exscan(lambda a, b: a + b, 0, flags) rightPositions= exrscan(lambda a, b: a + b, 0, notFlags) positions = map(lambda a, b, flag: a if flag elselen(input) - b - 1, leftPositions, rightPositions, flags) returnscatter(input, positions) 0 0 0-2 phases 0-2 2 2 • This code is decomposed into 3 phases • Copperhead compiler takes care of intermediate variables • Copperhead compiler uses shared memory for temporaries used in scans here • Everything else is in registers

27. Interpreting to Copperhead If the interpreter harvested dynamic type information, it could use the Copperhead compiler as a backend Fun project – see what kinds of information could be gleaned from the Python interpreter at runtime to figure out what should be compiled via Copperhead to a manycore chip

28. Future Work • Finish support for the basics • Compiler transformations • Nested data parallelism flattening • segmented scans • Retargetability • Thread Building Blocks/OpenMP/OpenCL • Bridge Python and Copperhead • Implement real algorithms with Copperhead • Vision/Machine Learning, etc.