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Item type:Publication, Access status: without files , QSLC: Quantization-Based, Low-Error Selective Approximation for GPUs(2021-02); ; GPUs use a large memory access granularity (MAG) that often results in a low effective compression ratio for memory compression techniques. The low effective compression ratio is caused by a significant fraction of compressed blocks that have a few bytes above a multiple of MAG. While MAG-aware selective approximation, based on a tree structure, has been used to increase the effective compression ratio and the performance gain, approximation results in a high error that is reduced by using complex optimizations. We propose a simple quantization-based approximation technique (QSLC) that can also selectively approximate a few bytes above MAG. While the quantization-based approximation technique has a similar performance to the state-of-the-art tree-based selective approximation, the average error for the quantization-based technique is 5× lower. We further trade-off the two techniques and show that the area and power overhead of the quantization-based technique is 12.1× and 7.6× lower than the state-of-the-art, respectively. Our sensitivity analysis to different block sizes further shows the opportunities and the significance of MAG-aware selective approximation.Publicationtype: Conference Paper25 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , SLC: Memory Access Granularity Aware Selective Lossy Compression for GPUs(2019-03); ; Memory compression is a promising approach for reducing memory bandwidth requirements and increasing performance, however, memory compression techniques often result in a low effective compression ratio due to large memory access granularity (MAG) exhibited by GPUs. Our analysis of the distribution of compressed blocks shows that a significant percentage of blocks are compressed to a size that is only a few bytes above a multiple of MAG, but a whole burst is fetched from memory. These few extra bytes significantly reduce the compression ratio and the performance gain that otherwise could result from a higher raw compression ratio. To increase the effective compression ratio, we propose a novel MAG aware Selective Lossy Compression (SLC) technique for GPUs. The key idea of SLC is that when lossless compression yields a compressed size with few bytes above a multiple of MAG, we approximate these extra bytes such that the compressed size is a multiple of MAG. This way, SLC mostly retains the quality of a lossless compression and occasionally trades small accuracy for higher performance. We show a speedup of up to 35% normalized to a state-of-the-art lossless compression technique with a low loss in accuracy. Furthermore, average energy consumption and energy-delay-product are reduced by 8.3% and 17.5%, respectively.Publicationtype: Conference Paper17 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Optimal DC/AC data bus inversion coding(2018-03); ; GDDR5 and DDR4 memories use data bus inversion (DBI) coding to reduce termination power and decrease the number of output transitions. Two main strategies exist for encoding data using DBI: DBI DC minimizes the number of outputs transmitting a zero, while DBI AC minimizes the number of signal transitions. We show that neither of these strategies is optimal and reduction of interface power of up to 6% can be achieved by taking both the number of zeros and the number of signal transitions into account when encoding the data. We then demonstrate that a hardware implementation of optimal DBI coding is feasible, results in a reduction of system power and requires only an insignificant additional die area.Publicationtype: Conference Paper24 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , E²MC: Entropy Encoding Based Memory Compression for GPUs(2017-06); ; Modern Graphics Processing Units (GPUs) provide much higher off-chip memory bandwidth than CPUs, but many GPU applications are still limited by memory bandwidth. Unfortunately, off-chip memory bandwidth is growing slower than the number of cores and has become a performance bottleneck. Thus, optimizations of effective memory bandwidth play a significant role for scaling the performance of GPUs. Memory compression is a promising approach for improving memory bandwidth which can translate into higher performance and energy efficiency. However, compression is not free and its challenges need to be addressed, otherwise the benefits of compression may be offset by its overhead. We propose an entropy encoding based memory compression (E2MC) technique for GPUs, which is based on the well-known Huffman encoding. We study the feasibility of entropy encoding for GPUs and show that it achieves higher compression ratios than state-of-the-art GPU compression techniques. Furthermore, we address the key challenges of probability estimation, choosing an appropriate symbol length for encoding, and decompression with low latency. The average compression ratio of E2MC is 53% higher than the state of the art. This translates into an average speedup of 20% compared to no compression and 8% higher compared to the state of the art. Energy consumption and energy-delayproduct are reduced by 13% and 27%, respectively. Moreover, the compression ratio achieved by E2MC is close to the optimal compression ratio given by Shannon's source coding theorem.Publicationtype: Conference Paper16 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , GPGPU workload characteristics and performance analysisGPUs are much more power-efficient devices compared to CPUs, but due to several performance bottlenecks, the performance per watt of GPUs is often much lower than what could be achieved theoretically. To sustain and continue high performance computing growth, new architectural and application techniques are required to create power-efficient computing systems. To find such techniques, however, it is necessary to study the power consumption at a detailed level and understand the bottlenecks which cause low performance. Therefore, in this paper, we study GPU power consumption at component level and investigate the bottlenecks that cause low performance and low energy efficiency. We divide the low performance kernels into low occupancy and full occupancy categories. For the low occupancy category, we study if increasing the occupancy helps in increasing performance and energy efficiency. For the full occupancy category, we investigate if these kernels are limited by memory bandwidth, coalescing efficiency, or SIMD utilization.Publicationtype: Conference Paper15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , How a single chip causes massive power bills GPUSimPow: A GPGPU power simulatorModern GPUs are true power houses in every meaning of the word: While they offer general-purpose (GPGPU) compute performance an order of magnitude higher than that of conventional CPUs, they have also been rapidly approaching the infamous 'power wall', as a single chip sometimes consumes more than 300W. Thus, the design space of GPGPU microarchitecture has been extended by another dimension: power. While GPU researchers have previously relied on cycle-accurate simulators for estimating performance during design cycles, there are no simulation tools that include power as well. To mitigate this issue, we introduce the GPUSimPow power estimation framework for GPGPUs consisting of both analytical and empirical models for regular and irregular hardware components. To validate this framework, we build a custom measurement setup to obtain power numbers from real graphics cards. An evaluation on a set of well-known benchmarks reveals an average relative error of 11.7% between simulated and hardware power for GT240 and an average relative error of 10.8% for GTX580. The simulator has been made available to the public [1].Publicationtype: Conference Paper22