A Survey on in-a-box parallel computing and its implications on system software research
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1) The document surveys research on parallel computing using multicore CPUs and GPUs, and its implications for system software. 2) It discusses parallel programming models like OpenMP, Intel TBB, CUDA, and OpenCL. It also covers research on optimizing memory allocation, reducing system call overhead, and revisiting OS architecture for manycore systems. 3) The document reviews work on supporting GPUs in virtualized environments through techniques like GPU virtualization. It also summarizes projects that utilize the GPU in middleware for tasks like network packet processing.
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A Survey on in-a-box parallel computing and its implications on system software research
- 1. A Survey on in-a-box parallel computing and its implications on system software research Changwoo Min (multics69@gmail.com)
- 2. Motivation Technology ratios matter, Jim Gray In the face of such "10X" forces, you can lose control of your destiny, Andrew S Grove What is the implications of multicore evolution for system software researcher?
- 3. Survey Scope and Strategy Parallel Parallel Application Middleware Parallel Programming Model System Library Operating System Multicore Multicore Machine Monitor Virtual GPGPU CPU CPU Multicore Multicore GPGPU GPGPU … CPU CPU
- 4. Contents Background Parallel Programming Model and Productivity Tools Optimization of System Software Supporting GPU in a Virtualized Environment Utilizing GPU in Middleware Conclusion
- 5. Background
- 6. Why multicore? Multicore CPU Power wall ILP(instruction level parallelism) wall Memory wall Wire delay GPGPU(General Purpose computing on a Graphic Processing Unit) GPU typically handles computation only for computer graphics. Add followings to the rendering pipelines programmable stages higher precision arithmetic Use stream processing on non-graphics data.
- 7. Architecture of GPGPU core
- 8. Parallel Programming Model and Productivity Tools
- 9. OpenMP Parallel Programming API for shared memory multiprocessing programming in C, C++, Fortran Use language extension – “#pragma omp” Need compiler support
- 10. OpenMP (cont’d) Fork-and-join model Bounded parallel loop, reduction Task-creation-and-join model Unbounded loop, recursive algorithm, producer/consumer
- 11. Intel TBB (Threading Building Block) Similar to OpenMP API for shared memory multiprocessing Fork-and-join parallel-for, parallel-reduce Task-creation-and-join Task scheduler Different from OpenMP C++ template library Concurrent container class Hash map, vector, queue Various synchronization mechanism mutex, spin lock, … Atomic type, atomic operations Scalable memory allocator
- 12. Nvidia CUDA (Compute Unified Device Architecture) CUDA Computing engine in Nvidia GPU Programming framework for Nvidia GPU Use CUDA extended C declspecs, keywords, intrinsic, runtime API, function launch, … CUDA extended C Compiling CUDA Code Processing flow on CUDA
- 13. Nvidia CUDA (cont’d) Execution Model Kernel Memory Access
- 14. OpenCL (Open Compute Language) CPU/GPU heterogeneous computing framework standardized by Khronous group OpenCL Memory Model CUDA, OpenCL Example
- 15. Lithe: Enabling Efficient Composition of Parallel Libraries Who? ParLab, UC Berkeley, HotPar’09 Problem Composition of parallel libraries shows performance anomaly
- 16. Lithe: Enabling Efficient Composition of Parallel Libraries (cont’d) Solution Virtualized thread are bad for parallel libraries. Harts Unvirtualized hardware thread context Sharing harts Lithe Cooperative hierarchical scheduler framework for harts
- 17. Concurrency bug detection: DataCollider Who? Microsoft Research, OSDI’10 Problem Detecting concurrency data race bug is difficult. For large system such as Windows kernel, runtime overhead is critical. Solution Sampling using code break point When a code break point is trapped, Set data break point for its operand Sleep for a while If the data is changed, it could be data race.
- 18. Concurrency bug detection: SyncFinder Who? UC San Diego, OSDI ’10 Problem How to find ad-hoc synchronization Solution Formalize patterns of ad-hoc synchronization Detect such patterns using LLVM
- 19. Optimization of System Software
- 20. Memory Allocation: Hoard Who? UT, ASPLOS’00 Problem Memory allocator is performance bottleneck in multi processor environment. Lock contention, False sharing, Blow up Allocator induced false sharing
- 21. Memory Allocation: Hoard (cont’d) Solution Per-processor heap to reduce lock contention and false sharing Global heap Borrow memory from global heap to increase per-processor heap Return memory to global heap if there are too much free memory in a per-processor heap
- 22. Memory Allocation: Xmalloc Who? UIUC, ICCIT’10 Problem Scalable malloc for CUDA whereby hundreds of threads run concurrently. Solution Memory allocation coalescing
- 23. System Call: FlexSC Who? University of Toronto, OSDI’10 Problem Negative performance impact of system call is huge. Direct cost + indirect cost Solution Batching, asynchronous system call
- 25. Multikernel Who? ETH Zurich, Microsoft Research Cambridge, SOSP’09 Problem System diversity It is no longer acceptable (or useful) to tune a general-purpose OS design for a particular hardware model.
- 26. Multikernel (cont’d) Problem (cont’d) SHM:stalled cycle (no locking!) The interconnects matters 8-socket Nahelem On-chip interconnects SHM vs. Message Passing Core diversity Programmable NICs GPU FPGA in CPU sockets
- 27. Multikernel (cont’d) Solution Today’s computer is already a distributed system. Why isn’t your OS? Barallelfish Implementation of the multikernel approach Message passing, shared nothing, replica maintenance
- 28. An Analysis of Linux Scalability to Many Cores Who? MIT CSAIL, OSDI’10 Problem If so, is Linux scalable enough? Solution Test linux scalability using 48 Intel cores with 7 applications No kernel problems up to 48 cores 3002 LOC patches Sloopy counter : replicated reference counter
- 29. Supporting GPU in a virtualized environment
- 30. HyVM (Hybrid Virtual Machines) Who? Georgia Tech Problem Asymmetries in performance, memory and cache Functional differences Multiple accelerators Vector processor Floating point Additional instructions for accelerations Solution heterogeneity- and asymmetry-aware hypervisors
- 31. HyVM (cont’d) Solution (cont’d) HyVM Architecture GViM: GPU Virtualization Architecture Memory management in GViM Harmony CPU/GPU co-scheduling
- 32. VMGL (Virtualizing OpenGL) Who? University of Toronto, VEE’07 Problem How to support OpenGL in a virtual machine environment Solution Forward OpenGL command to the driver domain
- 33. Utilizing GPU in Middleware
- 34. StoreGPU Who? University of British Columbia, HDPC’10 Problem In CAS(Contents Addressable Storage), How to minimizing hash calculation cost Solution Offloading to GPU StoreGPU Architecture
- 35. PacketShader Who? KAIST, SIGCOMM’10, NSDI’11 Problem How to boot up performance of software router Solution Offload stateless (parallelizable) packet processing to GPU PacketShader Architecture Basic Workflow of PacketShader
- 36. Conclusion
- 37. S