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This work investigates the memory capabilities of the NVIDIA DGX Spark, a novel platform featuring a unified memory architecture where DDR memory is located on the CPU and is fully accessible from the GPU.
Eiger is presented, a high-performance library for GPU-based data analytics that improves single-GPU query processing through runtime workload adaptivity and provides multiple implementation variants and tunable knobs for most operators.
Valk, a performance analysis tool that combines data from multiple profilers, shows that when memory bandwidth is increased, kernels become compute bound, and makes three recommendations to fully utilize the GPUs' potential for relational workloads when the memory wall is removed.
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