687 research outputs found
Mixed-Precision Random Projection for RandNLA on Tensor Cores
Random projection can reduce the dimension of data while capturing its
structure and is a fundamental tool for machine learning, signal processing,
and information retrieval, which deal with a large amount of data today.
RandNLA (Randomized Numerical Linear Algebra) leverages random projection to
reduce the computational complexity of low-rank decomposition of tensors and
solve least-square problems. While the computation of the random projection is
a simple matrix multiplication, its asymptotic computational complexity is
typically larger than other operations in a RandNLA algorithm. Therefore,
various studies propose methods for reducing its computational complexity. We
propose a fast mixed-precision random projection method on NVIDIA GPUs using
Tensor Cores for single-precision tensors. We exploit the fact that the random
matrix requires less precision, and develop a highly optimized matrix
multiplication between FP32 and FP16 matrices -- SHGEMM (Single and
Half-precision GEMM) -- on Tensor Cores, where the random matrix is stored in
FP16. Our method can compute Randomized SVD 1.28 times faster and Random
projection high order SVD 1.75 times faster than baseline single-precision
implementations while maintaining accuracy.Comment: PASC'2
Reducing shared memory footprint to leverage high throughput on Tensor Cores and its flexible API extension library
NVIDIA Tensor Core is a mixed-precision matrix-matrix multiplication and
addition computing unit, where the theoretical peak performance is more than
300 TFlop/s on NVIDIA A100 GPU. NVIDIA provides WMMA API for using Tensor Cores
in custom kernel functions. The most common way to use Tensor Core is to supply
the input matrices from shared memory, which has higher bandwidth than global
memory. However, the Bytes-per-Flops (B/F) ratio of the shared memory and
Tensor Cores is small since the performance of Tensor Cores is high. Thus, it
is important to reduce the shared memory footprint for efficient Tensor Cores
usage. In this paper, we analyze the simple matrix-matrix multiplication on
Tensor Cores by the roofline model and figure out that the bandwidth of shared
memory might be a limitation of the performance when using WMMA API. To
alleviate this issue, we provide a WMMA API extension library to boost the
throughput of the computation, which has two components. The first one allows
for manipulating the array of registers input to Tensor Cores flexibly. We
evaluate the performance improvement of this library. The outcome of our
evaluation shows that our library reduces the shared memory footprint and
speeds up the computation using Tensor Cores. The second one is an API for the
SGEMM emulation on Tensor Cores without additional shared memory usage. We have
demonstrated that the single-precision emulating batch SGEMM implementation on
Tensor Cores using this library achieves 54.2 TFlop/s on A100 GPU, which
outperforms the theoretical peak performance of FP32 SIMT Cores while achieving
the same level of accuracy as cuBLAS. The achieved throughput can not be
achieved without reducing the shared memory footprint done by our library with
the same amount of register usage.Comment: HPC Asia 202
DGEMM on Integer Matrix Multiplication Unit
Deep learning hardware achieves high throughput and low power consumption by
reducing computing precision and specializing in matrix multiplication. For
machine learning inference, fixed-point value computation is commonplace, where
the input and output values and the model parameters are quantized. Thus, many
processors are now equipped with fast integer matrix multiplication units
(IMMU). It is of significant interest to find a way to harness these IMMUs to
improve the performance of HPC applications while maintaining accuracy. We
focus on the Ozaki scheme, which computes a high-precision matrix
multiplication by using lower-precision computing units, and show the
advantages and disadvantages of using IMMU. The experiment using integer Tensor
Cores shows that we can compute double-precision matrix multiplication faster
than cuBLAS and an existing Ozaki scheme implementation on FP16 Tensor Cores on
NVIDIA consumer GPUs. Furthermore, we demonstrate accelerating a quantum
circuit simulation by up to 4.33 while maintaining the FP64 accuracy
Quantum Circuit Simulation by SGEMM Emulation on Tensor Cores and Automatic Precision Selection
Quantum circuit simulation provides the foundation for the development of
quantum algorithms and the verification of quantum supremacy. Among the various
methods for quantum circuit simulation, tensor network contraction has been
increasing in popularity due to its ability to simulate a larger number of
qubits. During tensor contraction, the input tensors are reshaped to matrices
and computed by a GEMM operation, where these GEMM operations could reach up to
90\% of the total calculation time. GEMM throughput can be improved by
utilizing mixed-precision hardware such as Tensor Cores, but straightforward
implementation results in insufficient fidelity for deep and large quantum
circuits. Prior work has demonstrated that compensated summation with special
care of the rounding mode can fully recover the FP32 precision of SGEMM even
when using TF32 or FP16 Tensor Cores. The exponent range is a critical issue
when applying such techniques to quantum circuit simulation. While TF32
supports almost the same exponent range as FP32, FP16 supports a much smaller
exponent range. In this work, we use the exponent range statistics of input
tensor elements to select which Tensor Cores we use for the GEMM. We evaluate
our method on Random Circuit Sampling (RCS), including Sycamore's quantum
circuit, and show that the throughput is 1.86 times higher at maximum while
maintaining accuracy.Comment: This paper has been accepted to ISC'2
Antibacterial Effects of Disulfiram in Helicobacter pylori
Background: Helicobacter pylori infection poses a risk of the occurrence of gastrointestinal diseases, such as gastric cancer. Its incidence rate is significantly reduced by eradication, and thereby, eradication therapy is generally performed. Disulfiram is an oral prescription drug mainly used for the treatment of alcohol dependence. In recent years, reports have been made on its anticancer and antibacterial effects, and thus, it has recently become an interesting subject. This study aimed to examine the antibacterial activity of disulfiram, investigate the presence or absence of its antibacterial activity on H. pylori, and determine whether it could be a new bactericidal drug against drug-resistant H. pylori.
Materials and Methods: Drug-sensitive strains of H. pylori and amoxicillin-resistant, clarithromycin-resistant, and metronidazole-resistant strains were used, and a growth inhibition test of H. pylori using disulfiram was performed. Furthermore, the expression of urease, vacuolating cytotoxin A (VacA), and CagA, the virulence proteins of H. pylori, was quantitatively analyzed using the Western blotting method. In addition, for H. pylori used in this study, the 16SrDNA sequence, a ribosomal gene involved in protein production, was analyzed to examine the presence or absence of gene mutation.
Results: Disulfiram suppressed the growth of 7 out of 12 H. pylori strains at 1 mu g/mL, and no correlation was observed between their susceptibility/resistance to current eradication antimicrobial drugs and disulfiram resistance. Disulfiram reduced the expression levels of urease, VacA, and CagA proteins. H. pylori, which showed resistance to disulfiram, tended to have fewer gene deletions/insertions in the 16S rDNA sequence; however, no specific mutation was detected. Conclusion: Disulfiram has a bactericidal effect on H. pylori at low concentrations, suggesting that it can be used as a supplement for current H. pylori eradication drugs
Development of Remotely Operated Vehicle for Small-size Jellyfish Extermination and its Evaluation of Extermination Motion Control
In recent years, increase in the number of jellyfish has caused damage in the fishery and tourism industries. Therefore, the extermination work of jellyfish is being carried out by human hands. However, conventional methods for extermination are required a lot of time and manpower. In this paper, we propose a method for extermination work of jellyfish using underwater robot. Also, we introduce developed ROV type underwater robot, which is called J.E.N.O.S. (Jellyfish Extermination Nifty-robot for Ocean Sustentation), and its extermination motion control. The ROV is developed in consideration of the attitude control during the extermination operation. Because, the attitude, such as surge and pitch angle, of ROV becomes unstable when performing jellyfish extermination. Therefore, we equipped 8 thrusters to improve attitude stability during the jellyfish extermination. As a result, surge acceleration is reduced to about 30.0%, and pitch angle velocity is reduced to about 25.8%.The 2022 International Conference on Artificial Life and Robotics (ICAROB 2022), January 20-23, 2022, on line, Oita, Japa
Expression of Flavone Synthase II and Flavonoid 3′-Hydroxylase Is Associated with Color Variation in Tan-Colored Injured Leaves of Sorghum
Clarithromycin Suppresses Human Respiratory Syncytial Virus Infection-Induced Streptococcus pneumoniae Adhesion and Cytokine Production in a Pulmonary Epithelial Cell Line
Human respiratory syncytial virus (RSV) sometimes causes acute and severe lower respiratory tract illness in infants and young children. RSV strongly upregulates proinflammatory cytokines and the platelet-activating factor (PAF) receptor, which is a receptor for Streptococcus pneumoniae, in the pulmonary epithelial cell line A549. Clarithromycin (CAM), which is an antimicrobial agent and is also known as an immunomodulator, significantly suppressed RSV-induced production of interleukin-6, interleukin-8, and regulated on activation, normal T-cell expressed and secreted (RANTES). CAM also suppressed RSV-induced PAF receptor expression and adhesion of fluorescein-labeled S. pneumoniae cells to A549 cells. The RSV-induced S. pneumoniae adhesion was thought to be mediated by the host cell's PAF receptor. CAM, which exhibits antimicrobial and immunomodulatory activities, was found in this study to suppress the RSV-induced adhesion of respiratory disease-causing bacteria, S. pneumoniae, to host cells. Thus, CAM might suppress immunological disorders and prevent secondary bacterial infections during RSV infection
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