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  1. Proceedings of Workshop on General Purpose Processing Using GPUs (GPGPU-7)
  2. KMA: A Dynamic Memory Manager for OpenCL
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Application-aware Memory System for Fair and Efficient Execution of Concurrent GPGPU Applications
KMA: A Dynamic Memory Manager for OpenCL
Efficient Instrumentation of GPGPU Applications Using Information Flow Analysis and Symbolic Execution
Measuring GPU Power with the K20 Built-in Sensor
Performance Evaluation and Optimization Mechanisms for Inter-operable Graphics and Computation on GPUs
GLZSS: LZSS Lossless Data Compression Can Be Faster
ad-heap: an Efficient Heap Data Structure for Asymmetric Multicore Processors
A CPU: GPU Hybrid Implementation and Model-Driven Scheduling of the Fast Multipole Method
ParallelJS: An Execution Framework for JavaScript on Heterogeneous Systems
APR: A Novel Parallel Repacking Algorithm for Efficient GPGPU Parallel Code Transformation
Power Modeling for Heterogeneous Processors
Exploiting GPU Hardware Saturation for Fast Compiler Optimization

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KMA: A Dynamic Memory Manager for OpenCL

Content Provider ACM Digital Library
Author Gaster, Benedict R. Spliet, Roy Howes, Lee Varbanescu, Ana Lucia
Abstract OpenCL is becoming a popular choice for the parallel programming of both multi-core CPUs and GPGPUs. One of the features missing in OpenCL, yet commonly required in irregular parallel applications, is dynamic memory allocation. In this paper, we propose KMA, a first dynamic memory allocator for OpenCL. KMA's design is based on a thorough analysis of a set of 11 algorithms, which shows that dynamic memory allocation is a necessary commodity, typically used for implementing complex data structures (arrays, lists, trees) that need constant restructuring at runtime. Taking into account both the survey findings and the status-quo of OpenCL, we design KMA as a two-layer memory manager that makes smart use of the patterns we identified in our application analysis: its basic functionality provides generic malloc() and free() APIs, while the higher layer provides support for building and efficiently managing dynamic data structures. Our experiments measure the performance and usability of KMA, using both microbenchmarks and a real-life case-study. Results show that when dynamic allocation is mandatory, KMA is a competitive allocator. We conclude that embedding dynamic memory allocation in OpenCL is feasible, but it is a complex, delicate task due to the massive parallelism of the platform and the portability issues between different OpenCL implementations.
Starting Page 9
Ending Page 18
Page Count 10
File Format PDF
ISBN 9781450327664
DOI 10.1145/2576779.2576781
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2014-03-01
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Opencl kernels Dynamic memory allocation Massive parallelism Multi-/many-cores
Content Type Text
Resource Type Article
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