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 …

Modern GPUs require an enormous register file (RF) to store the context of thousands of
active threads. It consumes considerable energy and contains multiple large banks to …

Graphics Processing Units (GPUs) use massive multi-threading coupled with static
scheduling to hide instruction latencies. Despite this, memory instructions pose a challenge …

Spin-transfer torque magnetic random-access memory (STT-MRAM) is an emerging
nonvolatile memory technology that has been received significant attention due to its higher …

Modern graphics processing units (GPUs) leverage a high degree of thread-level
parallelism, necessitating large-sized register files for storing numerous thread contexts. To …

Graphics processing units (GPUs) achieve high throughput by exploiting a high degree of
thread-level parallelism (TLP). To support such high TLP, GPUs have a large-sized register …

Graphics Processing Units (GPUs) employ large register files to accommodate all active
threads and accelerate context switching. Unfortunately, register files are a scalability …

Modern GPUs benefit from a sizable Register File (RF) to provide fine-grained thread
switching. As the RF is huge and accessed frequently, it consumes a considerable share of …

Modern GPUs require an enormous register file (RF) to store the context of thousands of
active threads. It consumes considerable energy and contains multiple large banks to …