Integrating the Par Lab Stack Running Damascene on SEJITS/ROS/RAMP Gold
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1 Integrating the Par Lab Stack Running on /ROS/RAMP Gold Kevin Klues, Yunsup Lee, Andrew Waterman Par Lab Winter Retreat 2010
2 Overall Goal of the Par Lab: Create Productive, Efficient, Correct, Portable SW for 100+ cores that scales as the core count increase every 2 years (!) Tremendous Progress has been made at individual layers in the Par Lab Software Stack This Demo is an attempt to demonstrate our integration effort across multiple layers
3 Diagnosing Power/Performance Correctness Integrating the Stack Personal Health Image Hearing, Speech Parallel Retrieval Music Browser Design Patterns/Motifs Composition & Coordination Language (C&CL) C&CL Compiler/Interpreter Static Verification Parallel Libraries Parallel Frameworks Type Systems Efficiency Sketching Languages Autotuners Legacy Communication & Schedulers Code Synch. Primitives Efficiency Language Compilers OS Libraries & Services Legacy OS Hypervisor Multicore/GPGPU ParLab Manycore/RAMP Directed Testing Dynamic Checking Debugging with Replay
4 - Parallel implementation of the Global Probability of Boundary (gpb) image contour detection algorithm
5 Nelson Morgan - Parallel implementation of the Global Probability of Boundary (gpb) image contour detection algorithm
6 Nelson Morgan - Parallel implementation of the Global Probability of Boundary (gpb) image contour detection algorithm TG, R=5
7 Nelson Morgan - Parallel implementation of the Global Probability of Boundary (gpb) image contour detection algorithm TG, R=5 Full gpb
8 Nelson Morgan TG, R=5 Full gpb - Parallel implementation of the Global Probability of Boundary (gpb) image contour detection algorithm - Algorithm developed by Prof. Jitendra Malik s Vision Group at UCB - Bryan Catanzaro et al. parallelized the code using CUDA, including some algorithmic changes (presented at last years winter retreat) - We ve now parallelized the same code in C and gotten it running in the python/ framework - This demo shows this parallelization effort in action - More details to come..
9 / - We picked a small subset of the full code and implemented it for in python using two parallel constructs
10 / - We picked a small subset of the full code and implemented it for in python using two parallel constructs - Since targets x86, NVIDIA GPUs, and SPARC v8 (i.e. RAMP Gold) the application can now run on all three platforms RAMP IA GPU
11 RAMP Gold - Par Lab architecture simulator on an FPGA - 64-core SPARC V8 target machine - Runtime-configurable memory hierarchy - 50 MIPS on Xilinx LX110T - 269x faster than comparable software simulator (SIMICS+GEMS) - Sufficient HW support (MMU, timer, ) to boot ROS, Linux 2.6 CORE I$ D$ 64 cores CORE I$ D$ Shared L2$ / Interconnect RAMP IA GPU DRAM
12 ROS (Tessellation) - New operating system designed for improved kernel scalability and better support for parallel applications ROS RAMP IA GPU
13 Time Space ROS (Tessellation) - New operating system designed for improved kernel scalability and better support for parallel applications - Built around idea of Space-Time Partitioning of Resources - Resources partitioned amongst execution entities based on explicit requests - Execution entities scheduled in both space and time based on meeting resource guarantees - Enables predictable application performance - Increases isolation ROS RAMP IA GPU
14 Time Space ROS RAMP IA GPU ROS (Tessellation) - New operating system designed for improved kernel scalability and better support for parallel applications - Built around idea of Space-Time Partitioning of Resources - Resources partitioned amongst execution entities based on explicit requests - Execution entities scheduled in both space and time based on meeting resource guarantees - Enables predictable application performance - Increases isolation - The Cell/Partition execution model - Multiple cores owned by a single cell - All cores gang scheduled - Exposed via the bthreads/harts ABI
15 C Runtime / Lithe - Support for newlib, glibc, and parlib - parlib is our internally developed C runtime supporting our new OS abstractions C Runtime Lithe bthreads ROS RAMP IA GPU
16 C Runtime Lithe bthreads ROS RAMP IA GPU C Runtime / Lithe - Support for newlib, glibc, and parlib - parlib is our internally developed C runtime supporting our new OS abstractions - Lithe support - Liquid threads framework - Enables multiple application thread schedulers to run cooperatively - Not yet fully integrated (but soon!) - bthreads / harts ABI - Tightly integrated with the OS, providing a userspace abstraction on top of its cell/partition model
17 C Runtime Lithe bthreads ROS RAMP IA Linux GPU C Runtime / Lithe - Support for newlib, glibc, and parlib - parlib is our internally developed C runtime supporting our new OS abstractions - Lithe support - Liquid threads framework - Enables multiple application thread schedulers to run cooperatively - Not yet fully integrated (but soon!) - bthreads / harts ABI - Tightly integrated with the OS, providing a userspace abstraction on top of its cell/partition model - Also support for legacy OS integration
18 DEMO
19 : Intermediate Steps RGB->L*a*b Color Conversion Convolve Filters L* a* b* K-means Multiple steps Multiple steps filters textons TG, R=5
20 Paths Through the Stack C Runtime Lithe / C Runtime Linux x86 bthreads ROS Linux RAMP IA GPU
21 Paths Through the Stack C Runtime Lithe Native C C Runtime ROS RAMP Gold bthreads ROS Linux RAMP IA GPU
22 Paths Through the Stack C Runtime Lithe Native C C Runtime ROS RAMP Gold bthreads ROS Linux Time for the Demo! (and questions while we set up) RAMP IA GPU
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