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Deep learning workload scheduling in gpu datacenters: A survey
Deep learning (DL) has demonstrated its remarkable success in a wide variety of fields. The
development of a DL model is a time-consuming and resource-intensive procedure. Hence …
development of a DL model is a time-consuming and resource-intensive procedure. Hence …
Splitwise: Efficient generative llm inference using phase splitting
Generative large language model (LLM) applications are growing rapidly, leading to large-
scale deployments of expensive and power-hungry GPUs. Our characterization of LLM …
scale deployments of expensive and power-hungry GPUs. Our characterization of LLM …
Deep learning workload scheduling in gpu datacenters: Taxonomy, challenges and vision
Deep learning (DL) shows its prosperity in a wide variety of fields. The development of a DL
model is a time-consuming and resource-intensive procedure. Hence, dedicated GPU …
model is a time-consuming and resource-intensive procedure. Hence, dedicated GPU …
Enzian: an open, general, CPU/FPGA platform for systems software research
Hybrid computing platforms, comprising CPU cores and FPGA logic, are increasingly used
for accelerating data-intensive workloads in cloud deployments, and are a growing topic of …
for accelerating data-intensive workloads in cloud deployments, and are a growing topic of …
{FpgaNIC}: An {FPGA-based} versatile 100gb {SmartNIC} for {GPUs}
Given that the increasing rate of network bandwidth is far ahead of that of the compute
capacity of host CPU, which by default processes network packets, SmartNIC has been …
capacity of host CPU, which by default processes network packets, SmartNIC has been …
Co-design hardware and algorithm for vector search
Vector search has emerged as the foundation for large-scale information retrieval and
machine learning systems, with search engines like Google and Bing processing tens of …
machine learning systems, with search engines like Google and Bing processing tens of …
Recpipe: Co-designing models and hardware to jointly optimize recommendation quality and performance
Deep learning recommendation systems must provide high quality, personalized content
under strict tail-latency targets and high system loads. This paper presents RecPipe, a …
under strict tail-latency targets and high system loads. This paper presents RecPipe, a …
Rm-ssd: In-storage computing for large-scale recommendation inference
To meet the strict service level agreement requirements of recommendation systems, the
entire set of embeddings in recommendation systems needs to be loaded into the memory …
entire set of embeddings in recommendation systems needs to be loaded into the memory …
{ACCL+}: an {FPGA-Based} Collective Engine for Distributed Applications
FPGAs are increasingly prevalent in cloud deployments, serving as Smart-NICs or network-
attached accelerators. To facilitate the development of distributed applications with FPGAs …
attached accelerators. To facilitate the development of distributed applications with FPGAs …
Mp-rec: Hardware-software co-design to enable multi-path recommendation
Deep learning recommendation systems serve personalized content under diverse tail-
latency targets and input-query loads. In order to do so, state-of-the-art recommendation …
latency targets and input-query loads. In order to do so, state-of-the-art recommendation …