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M2070Q, M2090, K10, K20 and K20X Modules
Performance of the M2090 Module
512 CUDA cores
655 Gigaflops of double-precision peak performance
1330 Gigaflops of single-precision peak performance
GDDR5 memory optimizes performance and reduces data transfers by keeping large data sets in 6 GB of local memory that is
attached directly to the GPU.
The NVIDIA Parallel DataCache™ accelerates algorithms such as physics solvers, ray-tracing, and sparse matrix multiplication
where data addresses are not known beforehand. This includes a configurable L1 cache per Streaming Multiprocessor block and
a unified L2 cache for all of the processor cores.
The NVIDIA GigaThread™ Engine maximizes the throughput by faster context switching that is 10X faster than the M1060
module, concurrent kernel execution, and improved thread block scheduling.
Asynchronous transfer turbo charges system performance by transferring data over the PCIe bus while the computing cores are
crunching other data. Even applications with heavy data-transfer requirements, such as seismic processing, can maximize the
computing efficiency by transferring data to local memory before it is needed.
The high speed PCIe Gen 2.0 data transfer maximizes bandwidth between the HP ProLiant server and the Tesla processors.
Performance of the K10 Module
3072 CUDA cores (1536 per GPU)
190 Gigaflops of double-precision peak performance (95 Gflops in each GPU)
4577 Gigaflops of single-precision peak performance (2288 Gigaflops in each GPU)
GDDR5 memory optimizes performance and reduces data transfers by keeping large data sets in 8 GB of local memory, 4 GB
attached directly to each GPU.
The NVIDIA Parallel DataCache™ accelerates algorithms such as physics solvers, ray-tracing, and sparse matrix multiplication
where data addresses are not known beforehand. This includes a configurable L1 cache per Streaming Multiprocessor block and
a unified L2 cache for all of the processor cores.
Asynchronous transfer turbo charges system performance by transferring data over the PCIe bus while the computing cores are
crunching other data. Even applications with heavy data-transfer requirements, such as seismic processing, can maximize the
computing efficiency by transferring data to local memory before it is needed.
The high speed PCIe Gen 3.0 data transfer maximizes bandwidth between the HP ProLiant server and the Tesla processors.
Performance of the K20 Module
2496 CUDA cores
1.17 Tflops of double-precision peak performance
3.52 Tflops of single-precision peak performance
GDDR5 memory optimizes performance and reduces data transfers by keeping large data sets in 5 GB of local memory that is
attached to the GPU
The NVIDIA Parallel DataCache™ accelerates algorithms such as physics solvers, ray-tracing, and sparse matrix multiplication
where data addresses are not known beforehand. This includes a configurable L1 cache per Streaming Multiprocessor block and
a unified L2 cache for all of the processor cores.
Asynchronous transfer turbo charges system performance by transferring data over the PCIe bus while the computing cores are
crunching other data. Even applications with heavy data-transfer requirements, such as seismic processing, can maximize the
computing efficiency by transferring data to local memory before it is needed.
Dynamic Parallelism capability that enables GPU threads to automatically spawn new threads.
Hyper-Q feature that enables multiple CPU cores to simultaneously utilize the CUDA cores on a single GPU.
The high speed PCIe Gen 2.0 data transfer maximizes bandwidth between the HP ProLiant server and the Tesla processors.
QuickSpecs
NVIDIA Tesla GPU Modules for HP ProLiant Servers
Standard Features
DA - 13743 North America — Version 16 — September 30, 2013
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