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The performance gap between CPU and memory widens continuously. Choosing the best memory layout for each hardware architecture is increasingly important as more and more programs become memory bound. For portable codes that run across heterogeneous hardware architectures, the choice of the memory layout for data structures is therefore ideally decoupled from the rest of a program. This can be accomplished via a zero-runtime-overhead abstraction layer, underneath which memory layouts can be freely exchanged. We present the C++ library LLAMA, which provides such a data structure abstraction layer with example implementations for multidimensional arrays of nested, structured data. LLAMA provides fully C++ compliant methods for defining and switching custom memory layouts for user-defined data types. Providing two close-to-life examples, we show that the LLAMA-generated AoS (Array of Struct) and SoA (Struct of Array) layouts produce identical code with the same performance characteristics as manually written data structures. LLAMAs layout-aware copy routines can significantly speed up transfer and reshuffling of data between layouts compared with naive element-wise copying. The library is fully extensible with third-party allocators and allows users to support their own memory layouts with custom mappings.
Deep learning (DL) workloads are moving towards accelerators for faster processing and lower cost. Modern DL accelerators are good at handling the large-scale multiply-accumulate operations that dominate DL workloads; however, it is challenging to ma
The training of deep neural networks (DNNs) is usually memory-hungry due to the limited device memory capacity of DNN accelerators. Characterizing the memory behaviors of DNN training is critical to optimize the device memory pressures. In this work,
This paper summarizes the idea of ChargeCache, which was published in HPCA 2016 [51], and examines the works significance and future potential. DRAM latency continues to be a critical bottleneck for system performance. In this work, we develop a low-
Java is the go-to programming language choice for developing scalable enterprise cloud applications. In such systems, even a few percent CPU time savings can offer a significant competitive advantage and cost saving. Although performance tools abound
In this paper we extend the concept of the traditional transactor, which focuses on correct content transfer, to a new timing-coherent transactor that also accurately aligns the timing of each transaction boundary so that designers can perform precis