This artifact contains the source code of DynaSOAr, a CUDA framework for Single-Method Multiple-Objects (SMMO) applications. SMMO is a type of object-oriented programs in which parallelism is expressed by running the same method on all applications of a type. DynaSOAr is a dynamic memory allocator, combined with a data layout DSL and a parallel do-all operation. This artifact provides a tutorial explaining the API of DynaSOAr, along with nine benchmark applications from different domains. All benchmarks can be configured to use a different memory allocator to allow for a comparison with other state-of-the-art memory allocators
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Real time image understanding and image generation require very large amounts of computing power. A ...
Multi-GPU machines are being increasingly used in high performance computing. These machines are bei...
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Dynaplex: Analyzing Program Complexity using Dynamically Inferred Recurrence Relations This artifact...
Abstract—There has been a growing trend in using heteroge-neous systems with CPUs and GPUs to solve ...
In Compute Unified Device Architecture (CUDA), programmers must manage memory operations, synchroniz...
Spatial architectures are more efficient than traditional Out-of-Order (OOO) processors for computat...
Parallelism is ubiquitous in modern computer architectures. Heterogeneity of CPU cores and deep memo...
International audience[Excerpt from the introduction] The spreading of Distributed Memory Parallel C...
Parallel programming requires a significant amount of developer effort, and creating optimized paral...
To achieve high performance on many-core architectures like GPUs, it is crucial to efficiently utili...
Object-oriented programming has long been regarded as too inefficient for SIMD high-performance comp...
ScatterAlloc is a dynamic memory allocator for the GPU. It is designed concerning the requirements o...
Algorithmic skeletons simplify software development: they abstract typical patterns of parallelism a...
In this paper, we analyze the special requirements of a dynamic memory allocator that is designed fo...
Real time image understanding and image generation require very large amounts of computing power. A ...
Multi-GPU machines are being increasingly used in high performance computing. These machines are bei...
Specialization is a promising direction for improving processor energy efficiency. With functionalit...
Dynaplex: Analyzing Program Complexity using Dynamically Inferred Recurrence Relations This artifact...
Abstract—There has been a growing trend in using heteroge-neous systems with CPUs and GPUs to solve ...
In Compute Unified Device Architecture (CUDA), programmers must manage memory operations, synchroniz...
Spatial architectures are more efficient than traditional Out-of-Order (OOO) processors for computat...
Parallelism is ubiquitous in modern computer architectures. Heterogeneity of CPU cores and deep memo...
International audience[Excerpt from the introduction] The spreading of Distributed Memory Parallel C...
Parallel programming requires a significant amount of developer effort, and creating optimized paral...
To achieve high performance on many-core architectures like GPUs, it is crucial to efficiently utili...