The Experts below are selected from a list of 14052 Experts worldwide ranked by ideXlab platform
Marco A. Z. Alves - One of the best experts on this subject based on the ideXlab platform.
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HIPE: HMC instruction predication extension applied on database processing
2018 Design Automation & Test in Europe Conference & Exhibition (DATE), 2018Co-Authors: Diego G. Tomé, Paulo C. Santos, Eduardo C. Almeida, Luigi Carro, Marco A. Z. AlvesAbstract:The recent Hybrid Memory Cube (HMC) is a smart memory which includes functional units inside one Logic Layer of the 3D stacked memory design. In order to execute instructions inside the Hybrid Memory Cube (HMC), the processor needs to send instructions to be executed near data, keeping most of the pipeline complexity inside the processor. Thus, control-flow and data-flow dependencies are all managed inside the processor, in such way that only update instructions are supported by the HMC. In order to solve data-flow dependencies inside the memory, previous work proposed HMC Instruction Vector Extensions (HIVE), which embeds a high number of functional units with a interlock register bank. In this work we propose HMC Instruction Prediction Extensions (HIPE), that supports predicated execution inside the memory, in order to transform control-flow dependencies into data-flow dependencies. Our mechanism focus on removing the high latency iteration between the processor and the smart memory during the execution of branches that depends on data processed inside the memory. In this paper we evaluate a balanced design of HIVE comparing to x86 and HMC executions. After we show the HIPE mechanism results when executing a database workload, which is a strong candidate to use smart memories. We show interesting trade-offs of performance when comparing our mechanism to previous work.
Diego G. Tomé - One of the best experts on this subject based on the ideXlab platform.
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HIPE: HMC instruction predication extension applied on database processing
2018 Design Automation & Test in Europe Conference & Exhibition (DATE), 2018Co-Authors: Diego G. Tomé, Paulo C. Santos, Eduardo C. Almeida, Luigi Carro, Marco A. Z. AlvesAbstract:The recent Hybrid Memory Cube (HMC) is a smart memory which includes functional units inside one Logic Layer of the 3D stacked memory design. In order to execute instructions inside the Hybrid Memory Cube (HMC), the processor needs to send instructions to be executed near data, keeping most of the pipeline complexity inside the processor. Thus, control-flow and data-flow dependencies are all managed inside the processor, in such way that only update instructions are supported by the HMC. In order to solve data-flow dependencies inside the memory, previous work proposed HMC Instruction Vector Extensions (HIVE), which embeds a high number of functional units with a interlock register bank. In this work we propose HMC Instruction Prediction Extensions (HIPE), that supports predicated execution inside the memory, in order to transform control-flow dependencies into data-flow dependencies. Our mechanism focus on removing the high latency iteration between the processor and the smart memory during the execution of branches that depends on data processed inside the memory. In this paper we evaluate a balanced design of HIVE comparing to x86 and HMC executions. After we show the HIPE mechanism results when executing a database workload, which is a strong candidate to use smart memories. We show interesting trade-offs of performance when comparing our mechanism to previous work.
Luigi Carro - One of the best experts on this subject based on the ideXlab platform.
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HIPE: HMC instruction predication extension applied on database processing
2018 Design Automation & Test in Europe Conference & Exhibition (DATE), 2018Co-Authors: Diego G. Tomé, Paulo C. Santos, Eduardo C. Almeida, Luigi Carro, Marco A. Z. AlvesAbstract:The recent Hybrid Memory Cube (HMC) is a smart memory which includes functional units inside one Logic Layer of the 3D stacked memory design. In order to execute instructions inside the Hybrid Memory Cube (HMC), the processor needs to send instructions to be executed near data, keeping most of the pipeline complexity inside the processor. Thus, control-flow and data-flow dependencies are all managed inside the processor, in such way that only update instructions are supported by the HMC. In order to solve data-flow dependencies inside the memory, previous work proposed HMC Instruction Vector Extensions (HIVE), which embeds a high number of functional units with a interlock register bank. In this work we propose HMC Instruction Prediction Extensions (HIPE), that supports predicated execution inside the memory, in order to transform control-flow dependencies into data-flow dependencies. Our mechanism focus on removing the high latency iteration between the processor and the smart memory during the execution of branches that depends on data processed inside the memory. In this paper we evaluate a balanced design of HIVE comparing to x86 and HMC executions. After we show the HIPE mechanism results when executing a database workload, which is a strong candidate to use smart memories. We show interesting trade-offs of performance when comparing our mechanism to previous work.
Paulo C. Santos - One of the best experts on this subject based on the ideXlab platform.
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HIPE: HMC instruction predication extension applied on database processing
2018 Design Automation & Test in Europe Conference & Exhibition (DATE), 2018Co-Authors: Diego G. Tomé, Paulo C. Santos, Eduardo C. Almeida, Luigi Carro, Marco A. Z. AlvesAbstract:The recent Hybrid Memory Cube (HMC) is a smart memory which includes functional units inside one Logic Layer of the 3D stacked memory design. In order to execute instructions inside the Hybrid Memory Cube (HMC), the processor needs to send instructions to be executed near data, keeping most of the pipeline complexity inside the processor. Thus, control-flow and data-flow dependencies are all managed inside the processor, in such way that only update instructions are supported by the HMC. In order to solve data-flow dependencies inside the memory, previous work proposed HMC Instruction Vector Extensions (HIVE), which embeds a high number of functional units with a interlock register bank. In this work we propose HMC Instruction Prediction Extensions (HIPE), that supports predicated execution inside the memory, in order to transform control-flow dependencies into data-flow dependencies. Our mechanism focus on removing the high latency iteration between the processor and the smart memory during the execution of branches that depends on data processed inside the memory. In this paper we evaluate a balanced design of HIVE comparing to x86 and HMC executions. After we show the HIPE mechanism results when executing a database workload, which is a strong candidate to use smart memories. We show interesting trade-offs of performance when comparing our mechanism to previous work.
Eduardo C. Almeida - One of the best experts on this subject based on the ideXlab platform.
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HIPE: HMC instruction predication extension applied on database processing
2018 Design Automation & Test in Europe Conference & Exhibition (DATE), 2018Co-Authors: Diego G. Tomé, Paulo C. Santos, Eduardo C. Almeida, Luigi Carro, Marco A. Z. AlvesAbstract:The recent Hybrid Memory Cube (HMC) is a smart memory which includes functional units inside one Logic Layer of the 3D stacked memory design. In order to execute instructions inside the Hybrid Memory Cube (HMC), the processor needs to send instructions to be executed near data, keeping most of the pipeline complexity inside the processor. Thus, control-flow and data-flow dependencies are all managed inside the processor, in such way that only update instructions are supported by the HMC. In order to solve data-flow dependencies inside the memory, previous work proposed HMC Instruction Vector Extensions (HIVE), which embeds a high number of functional units with a interlock register bank. In this work we propose HMC Instruction Prediction Extensions (HIPE), that supports predicated execution inside the memory, in order to transform control-flow dependencies into data-flow dependencies. Our mechanism focus on removing the high latency iteration between the processor and the smart memory during the execution of branches that depends on data processed inside the memory. In this paper we evaluate a balanced design of HIVE comparing to x86 and HMC executions. After we show the HIPE mechanism results when executing a database workload, which is a strong candidate to use smart memories. We show interesting trade-offs of performance when comparing our mechanism to previous work.
