The massive parallelism present in GPUs comes at the cost of reduced L1 and L2 cache
sizes per thread, leading to serious cache contention problems such as thrashing. Hence …

As modern GPU workloads grow in size and complexity, there is an ever-increasing demand
for GPU computational power. Emerging workloads contain hundreds or thousands of GPU …

Exploiting data locality in GPGPUs is critical for efficiently using the smaller data caches and
handling the memory bottleneck problem. This paper proposes a thread block-centric …

Chiplets are transforming computer system designs, allowing system designers to combine
heterogeneous computing resources at unprecedented scales. Breaking larger, mono-lithic …

The compute work rasterizer or the GigaThread Engine of a modern NVIDIA GPU focuses on
maximizing compute work occupancy across all streaming multiprocessors in a GPU while …

Contemporary GPUs are designed to handle long-latency operations effectively; however,
challenges such as core occupancy (number of warps in a core) and pipeline width can …

Memory wall is known as one of the most critical bottlenecks in processors, rooted in the
long memory access delay. With the advent of emerging memory-intensive applications …

Graphic processing units (GPU) concept, combined with CUDA and OpenCL programming
models, offers new opportunities to reduce latency and power consumption of throughput …

Contemporary GPU architectures have multiple shader cores and a scheduler that
distributes work (threads) among them, focusing on load balancing. These load balancing …

Sparse Matrix-Matrix Multiplication (SpMM) is a building-block operation in scientific
computing and machine learning applications. Recent advancements in hardware, notably …