Graphics Processing Units (GPUs) utilize memory hierarchy and Thread-Level Parallelism
(TLP) to tolerate off-chip memory latency, which is a significant bottleneck for memory-bound …

Registers are the fastest and simultaneously the most expensive kind of memory available to
GPU threads. Due to existence of a great number of concurrently executing threads, and the …

Modern GPUs require tens of thousands of concurrent threads to fully utilize the massive
amount of processing resources. However, thread concurrency in GPUs can be diminished …

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

With the skyrocketing advances of process technology, the increased need to process huge
amount of data, and the pivotal need for power efficiency, the usage of Graphics Processing …

Long memory latency and limited throughput become performance bottlenecks of GPGPU
applications. The latency takes hundreds of cycles which is difficult to be hidden by simply …

GPUs are widely used in today's computing platforms to accelerate applications in various
domains. However, scarce GPU memory resources are often the dominant limiting factor in …

Warp instructions with an identical arithmetic operation on same input values produce the
identical computation results. This paper proposes warp instruction reuse to allow such …

Albeit GPUs are supposed to be tolerant to long latency of data fetch operation, we observe
that L1 cache misses occur in a bursty manner for many memory-intensive applications. This …

Graphics processing units (GPUs) include a large amount of hardware resources for parallel
thread executions. However, the resources are not fully utilized during runtime, and …