Scalable Fine-Grained Parallel Cycle Enumeration Algorithms

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Dataflow programming is a methodology that enables the development of high-level, parametric programs that are independent of the underlying platform. This approach is particularly useful for heterogeneous platforms, as it eliminates the need to rewrite ap ...

Computing servers play a key role in the development and process of emerging compute-intensive applications in recent years. However, they need to operate efficiently from an energy perspective viewpoint, while maximizing the performance and lifetime of th ...

Field-programmable gate arrays (FPGAs) combine hardware reconfigurability with a high degree of parallelism. Consequently, FPGAs offer performance gains and power savings for many applications. A recent trend has been to leverage the hardware versatility o ...

Recent advances in computational neuroscience have demonstrated the usefulness and importance of stochastic, spatial reaction-diffusion simulations. However, ever increasing model complexity renders traditional serial solvers, as well as naive parallel imp ...

In order to develop sustainable and more powerful information technology (IT) infrastructures, the challenges posed by the "memory wall" are critical for the design of high-performance and high-efficiency many-core computing systems. In this context, recen ...

Developing and fine-tuning software programs for heterogeneous hardware such as CPU/GPU processing platforms comprise a highly complex endeavor that demands considerable time and effort of software engineers and requires evaluating various fundamental comp ...

Recent GPU architectures make available numbers of parallel processing units that exceed by orders of magnitude the ones offered by CPU architectures. Whereas programs written using dataflow programming languages are well suited for programming heterogeneo ...

As the data volume grows, reducing the query execution times remains an elusive goal. While approximate query processing (AQP) techniques present a principled method to trade off accuracy for faster queries in analytics, the sample creation is often consid ...

FPGAs are proving useful and attractive for many applications, thanks to their hardware reconfigurability, low power, and high-degree of parallelism. As a result, modern embedded systems are often based on systems-on-chip (SoCs), where CPUs and FPGAs share ...

Timely insights lead to business growth and scientific breakthroughs but require analytical engines that cope with the ever-increasing data processing needs. Analytical engines relied on rapid CPU improvements, yet the end of Dennard scaling stopped the fr ...

Scalable Fine-Grained Parallel Cycle Enumeration Algorithms

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Design Space Exploration for Partitioning Dataflow Program on CPU-GPU Heterogeneous System

Reinforcement Learning-Based Joint Reliability and Performance Optimization for Hybrid-Cache Computing Servers

DFAulted: Analyzing and Exploiting CPU Software Faults Caused by FPGA-Driven Undervolting Attacks

STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale

Full System Exploration of On-Chip Wireless Communication on Many-Core Architectures

Dynamic SIMD Parallel Execution on GPU from High-Level Dataflow Synthesis

Inter-actions parallel execution on GPU from high-level dataflow synthesis

Sampling-Based AQP in Modern Analytical Engines

FPGA-to-CPU Undervolting Attacks

Efficient Analytical Query Processing on CPU-GPU Hardware Platforms