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1. #Nvidia geforce gt 745m 2gb benchmark code#
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3. #Nvidia geforce gt 745m 2gb benchmark series#
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Cheaper and lower performing products were expected to be released over time.

#Nvidia geforce gt 745m 2gb benchmark series#

The GeForce 700 series for desktop architecture.

#Nvidia geforce gt 745m 2gb benchmark Pc#

GTX 780ti within a PC under an SLI configuration

#Nvidia geforce gt 745m 2gb benchmark free#

By giving kernels the ability to dispatch their own child kernels, GK110 can both save time by not having to go back to the CPU, and in the process free up the CPU to work on other tasks. With Fermi, only the CPU could dispatch a kernel, which incurs a certain amount of overhead by having to communicate back to the CPU. Dynamic parallelism ĭynamic parallelism ability is for kernels to be able to dispatch other kernels. Nvidia supports the DX12 API on all the DX11-class GPUs it has shipped these belong to the Fermi, Kepler and Maxwell architectural families. Nvidia Kepler GPUs of the GeForce 700 series fully support DirectX 11.0.

#Nvidia geforce gt 745m 2gb benchmark code#

By increasing the number of MPI jobs, it's possible to utilize Hyper-Q on these algorithms to improve the efficiency all without changing the code itself. As legacy MPI-based algorithms that were originally designed for multi-CPU systems that became bottlenecked by false dependencies now have a solution. The simple nature of Hyper-Q is further reinforced by the fact that it's easily map to MPI, a common message passing interface frequently used in HPC. By having 32 work queues, GK110 can in many scenarios, achieve higher utilization by being able to put different task streams on what would otherwise be an idle SMX. The significance of this being that having a single work queue meant that Fermi could be under occupied at times as there wasn't enough work in that queue to fill every SM. Hyper-Q expands GK110 hardware work queues from 1 to 32. Atomic operations are also overhauled, speeding up the execution speed of atomic operations and adding some FP64 operations that were previously only available for FP32 data. New shuffle instructions allow for threads within a warp to share data without going back to memory, making the process much quicker than the previous load/share/store method. New shuffle Instructions Īt a low level, GK110 sees additional instructions and operations to further improve performance. With a 48KB space, the texture cache can become a read-only cache for compute workloads. Register file space has increased to 256KB compared to Fermi. The SMX also sees an increase in space for register file. The single biggest change from GK104 is that rather than 8 dedicated FP64 CUDA cores, GK110 has up to 64, giving it 8x the FP64 throughput of a GK104 SMX. With GK110, Nvidia opted to increase compute performance.

The GeForce 700 series contains features from both GK104 and GK110.

ĭynamic Super Resolution (DSR) was added to Kepler GPUs with the latest Nvidia drivers.

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The series also supports DirectX 12 on Windows 10. Furthermore, error detection capabilities have been added to make it safer for use with workloads that rely on ECC.

With 48KB in size, in compute the texture cache becomes a read-only cache, specializing in unaligned memory access workloads. With GK110, Nvidia also reworked the GPU texture cache to be used for compute. This goes in hand with an increase of total number of registers each thread can address, moving from 63 registers per thread to 255 registers per thread with GK110. Performance in register-starved scenarios is also improved as there are more registers available to each thread. Both the L2 cache and register file bandwidth have also doubled. As for the L2 cache, GK110 L2 cache space increased by up to 1.5MB, 2x as big as GF110. At the SMX level, GK110's register file space has increased to 256KB composed of 64K 32bit registers, as compared to Fermi's 32K 32bit registers totaling 128 KB. With GK110, increases in memory space and bandwidth for both the register file and the L2 cache over previous models, are seen. This model also attempts to maximise energy efficiency through the execution of as many tasks as possible in parallel according to the capabilities of its streaming processors. GK110 was designed and marketed with computational performance in mind.