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Garuda-PM July 2011 Challenges and Opportunities in Heterogeneous Multi-core Era R. Govindarajan HPC Lab,SERC, IISc

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Presentation on theme: "Garuda-PM July 2011 Challenges and Opportunities in Heterogeneous Multi-core Era R. Govindarajan HPC Lab,SERC, IISc"— Presentation transcript:

1 Garuda-PM July 2011 Challenges and Opportunities in Heterogeneous Multi-core Era R. Govindarajan HPC Lab,SERC, IISc

2 Garuda-PM July 2011 © 2 Overview Introduction Trends in Processor Architecture Programming Challenges Data and Task Level Parallelism on Multi-cores Exploiting Data, Task and Pipeline Parallelism Data Parallelism on GPUs and Task Parallelism on CPUs Conclusions

3 Garuda-PM July 2011 © 3 Moores Law : Transistors

4 Garuda-PM July 2011 © 4 Moores Law : Performance Processor performance doubles every 1.5 years Processor performance doubles every 1.5 years

5 Garuda-PM July 2011 © 5 Overview Introduction Trends in Processor Architecture –Instruction Level Parallelism –Multi-Cores –Accelerators Programming Challenges Data and Task Level Parallelism on Multi-cores Exploiting Data, Task and Pipeline Parallelism Data Parallelism on GPUs and Task Parallelism on CPUs Conclusions

6 Garuda-PM July 2011 © 6 Progress in Processor Architecture More transistors New architecture innovations –Multiple Instruction Issue processors VLIW Superscalar EPIC –More on-chip caches, multiple levels of cache hierarchy, speculative execution, … Era of Instruction Level Parallelism

7 Garuda-PM July 2011 © 7 Moores Law: Processor Architecture Roadmap (Pre-2000) First P Super- scalar EPIC RISC VLIW ILP Processors

8 Garuda-PM July 2011 © 8 Multicores : The Right Turn 6 GHz 1 Core 3 GHz 1 Core 1 GHz 1 Core Performance 3 GHz 16 Core 3 GHz 4 Core 3 GHz 2 Core

9 Garuda-PM July 2011 © 9 Era of Multicores (Post 2000) Multiple cores in a single die Early efforts utilized multiple cores for multiple programs Throughput oriented rather than speedup- oriented!

10 Garuda-PM July 2011 © 10 Moores Law: Processor Architecture Roadmap (Post-2000) First P RISC VLIW Super- scalar EPIC Multi- cores

11 Garuda-PM July 2011 © 11 Progress in Processor Architecture More transistors New architecture innovations –Multiple Instruction Issue processors –More on-chip caches –Multi cores –Heterogeneous cores and accelerators Graphics Processing Units (GPUs) Cell BE, Clearspeed Larrabee or SCC Reconfigurable accelerators … Era of Heterogeneous Accelerators

12 Garuda-PM July 2011 © 12 Moores Law: Processor Architecture Roadmap (Post-2000) First P RISC VLIW Super- scalar EPIC Multi- cores Accele- rators

13 Garuda-PM July 2011 © 13 Accelerators

14 Garuda-PM July 2011 © 14 Accelerators: Hype or Reality? Some Top500 Systems (June 2011 List) RankSystemDescription# Procs.R_max (TFLOPS) 2TianheXeon + Nvidia Tesla C2050 GPUs ,566 4Nebulae- Dawning Intel X5650, NVidia Tesla C2050 GPU 55, ,960 1,271 7TsubameXeon + Nvidia GPU732781,192 10RoadrunnerOpteron + CellBE ,105

15 Garuda-PM July 2011 © 15 Accelerators – Cell BE

16 Garuda-PM July 2011 © 16 Accelerator – Fermi S2050

17 Garuda-PM July 2011 © 17 How is the GPU connected? C0 Memory C1C2C3 IMC SLC

18 Garuda-PM July 2011 © 18 Overview Introduction Trends in Processor Architecture Programming Challenges Data and Task Level Parallelism on Multi-cores Exploiting Data, Task and Pipeline Parallelism Data Parallelism on GPUs and Task Parallelism on CPUs Conclusions

19 Garuda-PM July 2011 © 19 Handling the Multi-Core Challenge Shared and Distributed Memory Programming Languages –OpenMP –MPI Other Parallel Languages (partitioned global address space languages) –X10, UPC, Chapel, … Emergence of Programming Languages for GPU –CUDA –OpenCL © 19

20 Garuda-PM July 2011 © 20 GPU Programming: Good News Emergence of Programming Languages for GPU –CUDA –OpenCL – Open Standards Growing collection of code base –CUDAzone –Packages supporting GPUs by ISV Impressive performance –Yes! What about Programmer Productivity? © 20

21 Garuda-PM July 2011 © 21 GPU Programming: Boon or Bane Challenges in GPU programming –Managing parallelism across SMs and SPMD cores –Transfer of data between CPU and GPU –Managing CPU-GPU memory bandwidth efficiently –Efficient use of different level of memory (GPU memory, Shared Memory, Constant and Texture Memory, … –Efficient buffer layout scheme to ensure all accesses to GPU memory are coalesced. –Identifying appropriate execution configuration for efficient execution –Synchronization across multiple SMs © 21

22 Garuda-PM July 2011 © 22 The Challenge : Data Parallelism Cg CUDA OpenCL AMD CAL

23 Garuda-PM July 2011 © 23 Challenge – Level 2 C0 Memory C1C2C3 MC SLC Synergistic Exec. on CPU & GPU Cores

24 Garuda-PM July 2011 © 24 Challenge – Level 3 Synergistic Exec. on Multiple Nodes Memory NIC N/W Switch Memory NIC Memory NIC

25 Garuda-PM July 2011 © 25 Data, Thread, & Task Level Parallelism MPI CUDA OpenCL OpenMP SSE CAL, PTX, …

26 Garuda-PM July 2011 © 26 Overview Introduction Trends in Processor Architecture Programming Challenges Data and Task Level Parallelism on Multi-cores –CUDA for Clusters Exploiting Data, Task and Pipeline Parallelism Data Parallelism on GPUs and Task Parallelism on CPUs Conclusions

27 Garuda-PM July 2011 © 27 Synergistic Execution on Multiple Hetergeneous Cores Our Apprach Compiler/ Runtime System CellBE Other Accel. Multicores GPUs SSE Streaming Lang. MPI OpenMP CUDA/ OpenCL Parallel Lang. Array Lang. (Matlab)

28 Garuda-PM July 2011 © CUDA on Other Platforms? CUDA programs are data parallel Cluster of multi-cores, typically for task parallel applications CUDA for multi-core SMPs (M-CUDA) CUDA on Cluster of multi-nodes having multi- cores?

29 Garuda-PM July 2011 © 29 CUDA on Clusters: Motivation Motivation CUDA, a natural way to write data-parallel programs Large Code-base (CUDA Zone) Emergence of OpenCL How can we leverage these on (existing) Clusters without GPUs? Challenges : Mismatch in parallelism granularity Synchronization and context switch can be more expensive!

30 Garuda-PM July 2011 © CUDA on Multi-Cores (M-CUDA) Memory Fuses threads in a thread block to a thread OMP Parallel for executing thread blocks in parallel Proper synchronization across kernels Loop Transformation techniques for efficient code Shared memory and synchronization inherent!

31 Garuda-PM July 2011 © M-CUDA Code Translation Grid >>(params) #pragma omp parallel for foreach block do { perBlockCode(params, blockIdx, blockDim, gridDim); }

32 Garuda-PM July 2011 © CUDA-For-Clusters CUDA kernels naturally express parallelism –Block level and thread-level Multiple levels of parallelism in hardware too! –Across multiple nodes –And across multiple cores within a node A runtime system to perform this work partitioning at multiple granularities. Little communication between block (in data parallel programs) Need some mechanism for –Global Shared memory across multiple nodes! –Synchronization across nodes A 'lightweight' software distributed shared memory layer to provide necessary abstraction

33 Garuda-PM July 2011 © CUDA-For-Clusters Consistency only at kernel boundaries Relaxed consistency model for global data Lazy Update CUDA memory mgmt. functions are linked to the DSM functions. Runtime system to ensure partitioning of tasks across cores OpenMP and MPI CUDA program Source-to-source transforms C program with block-level code specification MPI + OpenMP work distribution runtime Software Distributed Shared Memory Interconnect P1 P2 RAM P1 P2 RAM P1 P2 RAM B0,B1B2,B3B4,B5 B6,B7 N1N1 N2N2 N3N3 N4N4 B0 B1B2B3 B4B5B6 B7 B0 B1B2B3 B4B5B6 B7 P1 P2 RAM

34 Garuda-PM July 2011 © Experimental Setup Benchmarks from Parboil and Nvidia CUDA SDK suite 8-node Xeon clusters, each node having Dual Socket Quad Core (2 x 4 cores) Baseline M-CUDA on single node (8-cores)

35 Garuda-PM July 2011 © Experimental Results With Lazy Update

36 Garuda-PM July 2011 © 36 Overview Introduction Trends in Processor Architecture Programming Challenges Data and Task Level Parallelism on Multi-cores Exploiting Data, Task and Pipeline Parallelism –Stream Programs on GPU Data Parallelism on GPUs and Task Parallelism on CPUs Conclusions

37 Garuda-PM July 2011 © 37 © 37 Stream Programming Model Higher level programming model where nodes represent computation and channels communication (producer/consumer relation) between them. Exposes Pipelined parallelism and Task-level parallelism Synchronous Data Flow (SDF), Stream Flow Graph, StreamIT, Brook, … Compiling techniques for achieving rate-optimal, buffer-optimal, software-pipelined schedules Mapping applications to Accelerators such as GPUs and Cell BE.

38 Garuda-PM July 2011 © 38 © 38 StreamIT Example Program Dup. Splitter Bandpass Filter + Amplifier Combiner Signal Source Bandpass Filter + Amplifier 2 – Band Equalizer

39 Garuda-PM July 2011 © 39 © 39 Challenges on GPUs Work distribution between the multiprocessors –GPUs have hundreds of processors (SMs and SIMD units)! Exploiting task-level and data-level parallelism –Scheduling across the multiprocessors –Multiple concurrent threads in SM to exploit DLP Determining the execution configuration (number of threads for each filter) that minimizes execution time. Lack of synchronization mechanisms between the multiprocessors of the GPU. Managing CPU-GPU memory bandwidth efficiently

40 Garuda-PM July 2011 © 40 © 40 Stream Graph Execution Stream Graph Software Pipelined Execution A C D B SM1SM2SM3SM4 A1A2 A3A4 B1B2 B3B4 D1 C1 D2 C2 D3 C3 D4 C Pipeline Parallelism Task Parallelism Data Parallelism

41 Garuda-PM July 2011 © 41 © 41 Multithreading –Identify good execution configuration to exploit the right amount of data parallelism Memory –Efficient buffer layout scheme to ensure all accesses to GPU memory are coalesced. Task Partition between GPU and CPU cores Work scheduling and processor (SM) assignment problem. –Takes into account communication bandwidth restrictions Our Approach

42 Garuda-PM July 2011 © 42 © 42 Compiler Framework Execute Profile Runs Generate Code for Profiling Configuration Selection StreamIt Program Task Partitioning Task Partitioning ILP Partitioner Heuristic Partitioner Instance Partitioning Instance Partitioning Modulo Scheduling Code Generation CUDA Code + C Code

43 Garuda-PM July 2011 © 43 © 43 Experimental Results on Tesla > 52x> 32x> 65x

44 Garuda-PM July 2011 © 44 Overview Introduction Trends in Processor Architecture Programming Challenges Data and Task Level Parallelism on Multi-cores Exploiting Data, Task and Pipeline Parallelism Data Parallelism on GPUs and Task Parallelism on CPUs –MATLAB on CPU/GPU Conclusions

45 Garuda-PM July 2011 © 45 Compiling MATLAB to Heterogeneous Machines MATLAB is an array language extensively used for scientific computation Expresses data parallelism –Well suited for acceleration on GPUs Current solutions (Jacket, GPUmat) require user annotation to identify GPU friendly regions Our compiler, MEGHA (MATLAB Execution on GPU-based Heterogeneous Architectures), is fully automatic

46 Garuda-PM July 2011 © 46 Compiler Overview Frontend constructs an SSA intermediate representation (IR) from the input MATLAB code Type inference is performed on the SSA IR –Needed because MATLAB is dynamically typed Backend identifies GPU friendly kernels, decides where to run them and inserts reqd. data transfers Code Simplification Type Inference SSA Construction Frontend Kernel Identification Mapping and Scheduling Data Transfer Insertion Code Generation Backend

47 Garuda-PM July 2011 © 47 Backend : Kernel Identification Kernel identification identifies sets of IR statements (kernels) on which mapping and scheduling decisions are made Modeled as a graph clustering problem Takes into account several costs and benefits while forming kernels –Register utilization (Cost) –Memory utilization (Cost) –Intermediate array elimination (Benefit) –Kernel invocation overhead reduction (Benefit)

48 Garuda-PM July 2011 © 48 Backend : Scheduling and Transfer Insertion Assignment and scheduling is performed using a heuristic algorithm –Assigns kernels to the processor which minimizes its completion time –Uses a variant of list scheduling Data transfers for dependencies within a basic block are inserted during scheduling Transfers for inter basic block dependencies are inserted using a combination of data flow analysis and edge splitting

49 Garuda-PM July 2011 © 49 Results Benchmarks were run on a machine with an Intel Xeon 2.83GHz quad-core processor and a Tesla S1070. Generated code was compiled using nvcc version 2.3. MATLAB version 7.9 was used.

50 Garuda-PM July 2011 © 50 Overview Introduction Trends in Processor Architecture Programming Challenges Data and Task Level Parallelism on Multi-cores Exploiting Data, Task and Pipeline Parallelism Data Parallelism on GPUs and Task Parallelism on CPUs Conclusions

51 Garuda-PM July 2011 © 51 Summary Multi-cores and Heterogeneous accelerators present tremendous research opportunity in –Architecture –High Performance Computing –Compilers –Programming Languages & Models Challenge is to exploit Performance Keeping programmer Productivity high and Portability of code across platforms PPP is Important

52 Garuda-PM July 2011 Thank You !!


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