Numair Khan
With the advent of multi and many-core processors, communication has replaced computation as the performance bottleneck. Most current approaches to the problem try to tolerate memory access latency through a high amount of Thread-Level Parallelism. However, not all applications benefit from such techniques and there is a need to address the weakness of the underlying […]
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Sudipta Chattopadhyay, Petru Eles, Zebo Peng
Embedded and real-time software is often constrained by several temporal requirements. Therefore, it is important to design embedded software that meets the required performance goal. The inception of embedded graphics processing units (GPUs) brings fresh hope in developing high-performance embedded software which were previously not suitable for embedded platforms. Whereas GPUs use massive parallelism to […]
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Xinxin Mei, Kaiyong Zhao, Chengjian Liu, Xiaowen Chu
Memory access efficiency is a key factor for fully exploiting the computational power of Graphics Processing Units (GPUs). However, many details of the GPU memory hierarchy are not released by the vendors. We propose a novel fine-grained benchmarking approach and apply it on two popular GPUs, namely Fermi and Kepler, to expose the previously unknown […]
Mustafa Ali, Tarik Ozkul
Traditional ways of increasing computer performance has been increasing speed and bit size. Although this kept us going for more than half of a century, methodology has hit a major road block due to power consumption and heat dissipation. The remedy found for this problem has been creating multiple cores/heterogeneous computing systems on a single […]
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Bharath Subramanian Pichai
The proliferation of heterogeneous compute platforms, of which CPU/GPU is a prevalent example, necessitates a manageable programming model to ensure widespread adoption. A key component of this is a shared unified address space between the heterogeneous units to obtain the programmability benefits of virtual memory. Indeed, processor vendors have already begun embracing heterogeneous systems with […]
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R. Mokhtari, M. Stumm
GPUs offer an order of magnitude higher compute power and memory bandwidth than CPUs. GPUs therefore might appear to be well suited to accelerate computations that operate on voluminous data sets in independent ways; e.g., for transformations, filtering, aggregation, partitioning or other ”Big Data” style processing. Yet experience indicates that it is difficult, and often […]
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Akihiko Kasagi, Koji Nakano, and Yasuaki Ito
The Hierarchical Memory Machine (HMM) is a theoretical parallel computing model that captures the essence of computation on CUDA-enabled GPUs. The offline permutation is a task to copy numbers stored in an array a of size n to an array b of the same size along a permutation P given in advance. A conventional algorithm […]
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Minsoo Rhu, Michael Sullivan, Jingwen Leng, Mattan Erez
As GPU’s compute capabilities grow, their memory hierarchy increasingly becomes a bottleneck. Current GPU memory hierarchies use coarse-grained memory accesses to exploit spatial locality, maximize peak bandwidth, simplify control, and reduce cache meta-data storage. These coarse-grained memory accesses, however, are a poor match for emerging GPU applications with irregular control flow and memory access patterns. […]
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Thomas M. Baumann, Jose Gracia
Valgrind, and specifically the included tool Memcheck, offers an easy and reliable way for checking the correctness of memory operations in programs. This works in an unintrusive way where Valgrind translates the program into intermediate code and executes it on an emulated CPU. The heavy weight tool Memcheck uses this to keep a full shadow […]
Alexandru Pirjan
In this paper, there are depicted optimization solutions for the segmented sum algorithmic function, developed using the Compute Unified Device Architecture (CUDA), a powerful and efficient solution for optimizing a wide range of applications. The parallel-segmented sum is often used in building many data processing algorithms and through its optimization, one can improve the overall […]
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Leonid Djinevski, Sime Arsenovski, Sasko Ristov, Marjan Gusev
GPU devices offer great performance when dealing with algorithms that require intense computational resources. A developer can configure the L1 cache memory of the latest GPU Kepler architecture with different cache size and cache set associativity, per Streaming Multiprocessors (SM). The performance of the computation intensive algorithms can be affected by these cache parameters. In […]
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C.P.Patidar, Meena Sharma
In this paper we implement histogram computations on a Graphics Processing Unit (GPU). Our Histogram computations is implemented using compute unified device architecture (CUDA) which is a minimal extension to C/C++. In this development Histogram computations, computed on GPU’s global memory as well as on shared memory. We also perform Histogram computations on CPU and […]
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