With the rapid advancements in 3D game technology, workload characterization has become crucial for each new generation of games. The increased complexity of scenes in 3D games allows for stunning real-time visual quality. However, handling such workloads results in significant power consumption over the GPU rendering pipeline. The focus of the current paper is low power optimization, targeting texture memory, geometry engine, pixel, and rasterization, as these components are significant contributors to the power consumption of a typical GPU. The proposed methodology integrates the Dynamic Voltage Frequency Scaling (DVFS) technique, adjusting voltage and frequency based on the workload analysis of frame rates with respect to the scenes of 3D games. Frame rates of 60 fps and 30 fps are set up to understand and manage the workload on frames. Furthermore, for comparative analysis, various frame-level power analysis schemes such as No DVFS implemented, Frame History Method, Frame Signature Method, and Tiled History-based are introduced. The proposed scheme consistently surpasses these frame-level schemes, with fewer missed deadlines, while having the lowest energy consumption per frame rate. The implementation resulted in a remarkable 65% improvement in quality, indicated by a reduction in deadline misses, along with a substantial 60% energy saving.

GPU accelerators are massively parallel in nature and tailored for processing numerically intensive high-performance computing applications. But most of the applications that involve heavy computations take excess time to process as the dataset gets larger and lead to more power consumption. Hence, among all the factors in sustainable computing that contribute to operartional costs of GPU, power and time management is one of the major challenging issue. This paper presents a methodology to reduce power consumption in GPUs, meanwhile keeping parallel execution of jobs as high as possible. To achieve this, a power and time aware framework is created by integrating TensorFlow library, InterPSS, and Dynamic Voltage Frequency Scaling (DVFS) techniques. For most of the scientific computing, matrix operations are considered as the fundamental building block. So, the performance, power consumption, and power efficiency of GPU for matrix applications are analyzed under proposed model. Experimental results reveal that proposed methodology substantially reduces peak power of GPUs by 20%, with improved speedup in execution time around 15%.

Byzantine Fault Tolerance (BFT) consensus mechanisms are vital for ensuring reliability in permissioned blockchain networks. However, existing BFT implementations such as Quorum-based BFT (QBFT), Istanbul BFT (IBFT) 2.0, and CLIQUE within the Hyperledger Besu framework face significant challenges, including high resource consumption, limited throughput, and scalability bottlenecks, particularly under complex and high-volume smart contract workloads. In this paper, we propose Dynamic Adaptive Byzantine Fault Tolerance (DABFT), a novel consensus framework designed to address these limitations through Adaptive Quorum Selection (AQS) and Optimized Transaction Batching (OTB). We evaluate DABFT using Hyperledger Caliper, benchmarking its performance against established BFT protocols under diverse transaction loads and operational conditions. The results demonstrate that DABFT achieves up to 30% higher throughput, 25% lower latency, and a 15% reduction in resource usage compared to QBFT. These improvements highlight DABFT’s potential to significantly enhance the performance, scalability, and resource efficiency of enterprise-grade blockchain deployments.